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4. MIDRC-MetricTree: a decision tree-based tool for recommending performance metrics in artificial intelligence-assisted medical image analysis.

Author

Drukker, Karen, Sahiner, Berkman, Hu, Tingting, Kim, Grace Hyun, Whitney, Heather M, Baughan, Natalie, Myers, Kyle J, Giger, Maryellen L, and McNitt-Gray, Michael

Subjects
artificial intelligence, computer-aided diagnosis, machine learning, performance evaluation, Clinical sciences, Biomedical engineering
Abstract

PURPOSE: The Medical Imaging and Data Resource Center (MIDRC) was created to facilitate medical imaging machine learning (ML) research for tasks including early detection, diagnosis, prognosis, and assessment of treatment response related to the coronavirus disease 2019 pandemic and beyond. The purpose of this work was to create a publicly available metrology resource to assist researchers in evaluating the performance of their medical image analysis ML algorithms. APPROACH: An interactive decision tree, called MIDRC-MetricTree, has been developed, organized by the type of task that the ML algorithm was trained to perform. The criteria for this decision tree were that (1) users can select information such as the type of task, the nature of the reference standard, and the type of the algorithm output and (2) based on the user input, recommendations are provided regarding appropriate performance evaluation approaches and metrics, including literature references and, when possible, links to publicly available software/code as well as short tutorial videos. RESULTS: Five types of tasks were identified for the decision tree: (a) classification, (b) detection/localization, (c) segmentation, (d) time-to-event (TTE) analysis, and (e) estimation. As an example, the classification branch of the decision tree includes two-class (binary) and multiclass classification tasks and provides suggestions for methods, metrics, software/code recommendations, and literature references for situations where the algorithm produces either binary or non-binary (e.g., continuous) output and for reference standards with negligible or non-negligible variability and unreliability. CONCLUSIONS: The publicly available decision tree is a resource to assist researchers in conducting task-specific performance evaluations, including classification, detection/localization, segmentation, TTE, and estimation tasks.

Published
2024

5. Back Cover

Author

Aley, Ginette, Anderson, Joseph L., Barker, Brett, Davis, William C., Etcheson, Nicole, and Gray, Michael P.

Published
2013

7. Index

Author

Aley, Ginette, Anderson, Joseph L., Barker, Brett, Davis, William C., Etcheson, Nicole, and Gray, Michael P.

Published
2013

9. Contributors

Author

Aley, Ginette, Anderson, Joseph L., Barker, Brett, Davis, William C., Etcheson, Nicole, and Gray, Michael P.

Published
2013

11. Gallery of Illustrations

Author

Aley, Ginette, Anderson, Joseph L., Barker, Brett, Davis, William C., Etcheson, Nicole, and Gray, Michael P.

Published
2013

12. Frontpiece

Author

Aley, Ginette, Anderson, Joseph L., Barker, Brett, Davis, William C., Etcheson, Nicole, and Gray, Michael P.

Published
2013

14. Acknowledgments

Author

Aley, Ginette, Anderson, Joseph L., Barker, Brett, Davis, William C., Etcheson, Nicole, and Gray, Michael P.

Published
2013

18. Title Page

Author

Aley, Ginette, Anderson, Joseph L., Barker, Brett, Davis, William C., Etcheson, Nicole, and Gray, Michael P.

Published
2013

21. OceanNet: A principled neural operator-based digital twin for regional oceans

Author

Chattopadhyay, Ashesh, Gray, Michael, Wu, Tianning, Lowe, Anna B., and He, Ruoying

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Nonlinear Sciences - Chaotic Dynamics, Physics - Atmospheric and Oceanic Physics
Abstract

While data-driven approaches demonstrate great potential in atmospheric modeling and weather forecasting, ocean modeling poses distinct challenges due to complex bathymetry, land, vertical structure, and flow non-linearity. This study introduces OceanNet, a principled neural operator-based digital twin for ocean circulation. OceanNet uses a Fourier neural operator and predictor-evaluate-corrector integration scheme to mitigate autoregressive error growth and enhance stability over extended time scales. A spectral regularizer counteracts spectral bias at smaller scales. OceanNet is applied to the northwest Atlantic Ocean western boundary current (the Gulf Stream), focusing on the task of seasonal prediction for Loop Current eddies and the Gulf Stream meander. Trained using historical sea surface height (SSH) data, OceanNet demonstrates competitive forecast skill by outperforming SSH predictions by an uncoupled, state-of-the-art dynamical ocean model forecast, reducing computation by 500,000 times. These accomplishments demonstrate the potential of physics-inspired deep neural operators as cost-effective alternatives to high-resolution numerical ocean models., Comment: Supplementary information can be found in: https://drive.google.com/file/d/1NoxJLa967naJT787a5-IfZ7f_MmRuZMP/view?usp=sharing

Published
2023

22. Quantitative Computed Tomography Lung COVID Scores with Laboratory Markers: Utilization to Predict Rapid Progression and Monitor Longitudinal Changes in Patients with Coronavirus 2019 (COVID-19) Pneumonia.

Author

Kang, Da, Kim, Grace, Park, Sa-Beom, Lee, Song-I, Koh, Jeong, Brown, Matthew, Abtin, Fereidoun, Goldin, Jonathan, Lee, Jeong, and Mcnitt-Gray, Michael

Subjects
coronavirus disease 2019 (COVID-19), prediction, quantitative computed tomography (CT) score, rapid progression
Abstract

Coronavirus disease 2019 (COVID-19), is an ongoing issue in certain populations, presenting rapidly worsening pneumonia and persistent symptoms. This study aimed to test the predictability of rapid progression using radiographic scores and laboratory markers and present longitudinal changes. This retrospective study included 218 COVID-19 pneumonia patients admitted at the Chungnam National University Hospital. Rapid progression was defined as respiratory failure requiring mechanical ventilation within one week of hospitalization. Quantitative COVID (QCOVID) scores were derived from high-resolution computed tomography (CT) analyses: (1) ground glass opacity (QGGO), (2) mixed diseases (QMD), and (3) consolidation (QCON), and the sum, quantitative total lung diseases (QTLD). Laboratory data, including inflammatory markers, were obtained from electronic medical records. Rapid progression was observed in 9.6% of patients. All QCOVID scores predicted rapid progression, with QMD showing the best predictability (AUC = 0.813). In multivariate analyses, the QMD score and interleukin(IL)-6 level were important predictors for rapid progression (AUC = 0.864). With >2 months follow-up CT, remained lung lesions were observed in 21 subjects, even after several weeks of negative reverse transcription polymerase chain reaction test. AI-driven quantitative CT scores in conjugation with laboratory markers can be useful in predicting the rapid progression and monitoring of COVID-19.

Published
2024

27. Coronal Heating as Determined by the Solar Flare Frequency Distribution Obtained by Aggregating Case Studies

Author

Mason, James Paul, Werth, Alexandra, West, Colin G., Youngblood, Allison A., Woodraska, Donald L., Peck, Courtney, Lacjak, Kevin, Frick, Florian G., Gabir, Moutamen, Alsinan, Reema A., Jacobsen, Thomas, Alrubaie, Mohammad, Chizmar, Kayla M., Lau, Benjamin P., Dominguez, Lizbeth Montoya, Price, David, Butler, Dylan R., Biron, Connor J., Feoktistov, Nikita, Dewey, Kai, Loomis, N. E., Bodzianowski, Michal, Kuybus, Connor, Dietrick, Henry, Wolfe, Aubrey M., Guerrero, Matt, Vinson, Jessica, Starbuck, Peter, Litton, Shelby D, Beck, M. G., Fisch, Jean-Paul, West, Ayana, Muniz, Alexis A., Chavez, Luis, Upthegrove, Zachary T., Runyon, Brenton M., Salazar, J., Kritzberg, Jake E., Murrel, Tyler, Ho, Ella, LaFemina, Quintin Y., Elbashir, Sara I., Chang, Ethan C., Hudson, Zachary A., Nussbaum, Rosemary O., Kennedy, Kellen, Kim, Kevin, Arango, Camila Villamil, Albakr, Mohammed A., Rotter, Michael, Garscadden, A. J., Salcido-Alcontar JR, Antonio, Pearl, Harrison M., Stepaniak, Tyler, Marquez, Josie A., Marsh, Lauren, Andringa, Jesse C, Osogwin, Austin, Shields, Amanda M., Brookins, Sarah, Hach, Grace K., Clausi, Alexis R., Millican, Emily B., Jaimes, Alan A, Graham, Alaina S., Burritt, John J., Perez, J. S., Ramirez, Nathaniel, Suri, Rohan, Myer, Michael S., Kresek, Zoe M., Goldsberry, C. A., Payne, Genevieve K., Jourabchi, Tara, Hu, J., Lucca, Jeffrey, Feng, Zitian, Gilpatrick, Connor B., Khan, Ibraheem A., Warble, Keenan, Sweeney, Joshua D., Dorricott, Philip, Meyer, Ethan, Kothamdi, Yash S., Sohail, Arman S., Grell, Kristyn, Floyd, Aidan, Bard, Titus, Mathieson, Randi M., Reed, Joseph, Cisneros, Alexis, Payne, Matthew P., Jarriel, J. R., Mora, Jacqueline Rodriguez, Sundell, M. E., Patel, Kajal, Alesmail, Mohammad, Alnasrallah, Yousef A, Abdullah, Jumana T., Molina-Saenz, Luis, Tayman, K. E., Brown, Gabriel T., Kerr-Layton, Liana, Berriman-Rozen, Zachary D., Hiatt, Quinn, Kalra, Etash, Ong, Jason, Vadayar, Shreenija, Shannahan, Callie D., Benke, Evan, zhang, Jinhua, Geisman, Jane, Martyr, Cara, Ameijenda, Federico, Akruwala, Ushmi H., Nehring, Molly, Kissner, Natalie, Rule, Ian C., Learned, Tyler, Smith, Alexandra N., Mazzotta, Liam, Rounsefell, Tyndall, Eyeson, Elizabeth A., Shelby, Arlee K., Moll, Tyler S, Menke, Riley, Shahba, Hannan, House Jr., Tony A., Clark, David B., Burns, Annemarie C., de La Beaujardiere, Tristan, Trautwein, Emily D., Plantz, Will, Reeves, Justin, Faber, Ian, Buxton, B. W., Highhouse, Nigel, Landrey, Kalin, Hansen, Connor M, Chen, Kevin, Hales, Ryder Buchanan, Borgerding, Luke R., Guo, Mutian, Crow, Christian J., Whittall, Lloyd C., Simmons, Conor, Folarin, Adeduni, Parkinson, Evan J., Rahn, Anna L., Blevins, Olivia, Morelock, Annalise M., Kelly, Nicholas, Parker, Nathan L., Smith, Kelly, Plzak, Audrey E., Saeb, David, Hares, Cameron T., Parker, Sasha R., McCoy, Andrew, Pham, Alexander V., Lauzon, Megan, Kennedy, Cayla J., Reyna, Andrea B., Acosta, Daniela M. Meza, Cool, Destiny J., Steinbarth, Sheen L., Mendoza-Anselmi, Patricia, Plutt, Kaitlyn E., Kipp, Isabel M, Rakhmonova, M., Brown, Cameron L., Van Anne, Gabreece, Moss, Alexander P., Golden, Olivia, Kirkpatrick, Hunter B., Colleran, Jake R., Sullivan, Brandon J, Tran, Kevin, Carpender, Michael Andrew, Mundy, Aria T., Koenig, Greta, Oudakker, Jessica, Engelhardt, Rasce, Ales, Nolan, Wexler, Ethan Benjamin, Beato, Quinn I, Chen, Lily, Cochran, Brooke, Hill, Paula, Hamilton, Sean R., Hashiro, Kyle, Khan, Usman, Martinez, Alexa M., Brockman, Jennifer L., Mallory, Macguire, Reed, Charlie, Terrile, Richard, Singh, Savi, Watson, James Adam, Creany, Joshua B., Price, Nicholas K., Miften, Aya M., Tran, Bryn, Kamenetskiy, Margaret, Martinez, Jose R., Opp, Elena N., Huang, Jianyang, Fails, Avery M., Belei, Brennan J., Slocum, Ryan, Astalos, Justin, East, Andrew, Nguyen, Lena P., Pherigo, Callie C, East, Andrew N., Li, David Y., Nelson, Maya LI, Taylor, Nicole, Odbayar, Anand, Rives, Anna Linnea, Mathur, Kabir P., Billingsley, Jacob, Polikoff, Hyden, Driscoll, Michael, Wilson, Orion K., Lahmers, Kyle, Toon, Nathaniel J., Lippincott, Sam, Musgrave, Andrew J., Gregory, Alannah H., Pitsuean-Meier, Sedique, Jesse, Trevor, Smith, Corey, Miles, Ethan J., Kainz, Sabrina J. H. T., Ji, Soo Yeun, Nguyen, Lena, Aryan, Maryam, Dinser, Alexis M., Shortman, Jadon, Bastias, Catalina S, Umbricht, Thomas D, Cage, Breonna, Randolph, Parker, Pollard, Matthew, Simone, Dylan M., Aramians, Andrew, Brecl, Ariana E., Robert, Amanda M., Zenner, Thomas, Saldi, Maxwell, Morales, Gavin, Mendez, Citlali, Syed, Konner, Vogel, Connor Maklain, Cone, Rebecca A., Berhanu, Naomi, Carpenter, Emily, Leoni, Cecilia, Bryan, Samuel, Ramachandra, Nidhi, Shaw, Timothy, Lee, E. C., Monyek, Eli, Wegner, Aidan B., Sharma, Shajesh, Lister, Barrett, White, Jamison R., Willard, John S., Sulaiman, S. A, Blandon, Guillermo, Narayan, Anoothi, Ruger, Ryan, Kelley, Morgan A., Moreno, Angel J., Balcer, Leo M, Ward-Chene, N. R. D., Shelby, Emma, Reagan, Brian D., Marsh, Toni, Sarkar, Sucheta, Kelley, Michael P., Fell, Kevin, Balaji, Sahana, Hildebrand, Annalise K., Shoha, Dominick, Nandu, Kshmya, Tucker, Julia, Cancio, Alejandro R., Wang, Jiawei, Rapaport, Sarah Grace, Maravi, Aimee S., Mayer, Victoria A., Miller, Andrew, Bence, Caden, Koke, Emily, Fauntleroy, John T, Doermer, Timothy, Al-Ghazwi, Adel, Morgan, Remy, Alahmed, Mohammed S., Mathavan, Adam Izz Khan Mohd Reduan, Silvester, H. K., Weiner, Amanda M., Liu, Nianzi, Iovan, Taro, Jensen, Alexander V., AlHarbi, Yazeed A., Jiang, Yufan, Zhang, Jiaqi, Jones, Olivia M., Huang, Chenqi, Reh, Eileen N., Alhamli, Dania, Pettine, Joshua, Zhou, Chongrui, Kriegman, Dylan, Yang, Jianing, Ash, Kevin, Savage, Carl, Kaiser, Emily, Augenstein, Dakota N., Padilla, Jacqueline, Stark, Ethan K., Hansen, Joshua A., Kokes, Thomas, Huynh, Leslie, Sanchez-Sanchez, Gustavo, Jeseritz, Luke A., Carillion, Emma L., Vepa, Aditya V., Khanal, Sapriya, Behr, Braden, Martin, Logan S., McMullan, Jesse J., Zhao, Tianwei, Williams, Abigail K., Alqabani, Emeen, Prinster, Gale H., Horne, Linda, Ruggles-Delgado, Kendall, Otto, Grant, Gomez, Angel R., Nguyen, Leonardo, Brumley, Preston J., Venegas, Nancy Ortiz, Varela, Ilian, Brownlow, Jordi, Cruz, Avril, Leiker, Linzhi, Batra, Jasleen, Hutabarat, Abigail P., Nunes-Valdes, Dario, Jameson, Connor, Naqi, Abdulaziz, Adams, Dante Q., Biediger, Blaine B., Borelli, William T, Cisne, Nicholas A., Collins, Nathaniel A., Curnow, Tyler L., Gopalakrishnan, Sean, Griffin, Nicholas F., Herrera, Emanuel, McGarvey, Meaghan V., Mellett, Sarah, Overchuk, Igor, Shaver, Nathan, Stratmeyer, Cooper N., Vess, Marcus T., Juels, Parker, Alyami, Saleh A., Gale, Skylar, Wallace, Steven P., Hunter, Samuel C, Lonergan, Mia C., Stewart, Trey, Maksimuk, Tiffany E., Lam, Antonia, Tressler, Judah, Napoletano, Elena R., Miller, Joshua B., Roy, Marc G., Chanders, Jasey, Fischer, Emmalee, Croteau, A. J., Kuiper, Nicolas A., Hoffman, Alex, DeBarros, Elyse, Curry, Riley T., Brzostowicz, A., Courtney, Jonas, Zhao, Tiannie, Szabo, Emi, Ghaith, Bandar Abu, Slyne, Colin, Beck, Lily, Quinonez, Oliver, Collins, Sarah, Madonna, Claire A., Morency, Cora, Palizzi, Mallory, Herwig, Tim, Beauprez, Jacob N., Ghiassi, Dorsa, Doran, Caroline R., Yang, Zhanchao, Padgette, Hannah M., Dicken, Cyrus A., Austin, Bryce W., Phalen, Ethan J., Xiao, Catherine, Palos, Adler, Gerhardstein, Phillip, Altenbern, Ava L., Orbidan, Dan, Dorr, Jackson A., Rivas, Guillermo A., Ewing, Calvin A, Giebner, B. C., McEntee, Kelleen, Kite, Emily R., Crocker, K. A., Haley, Mark S., Lezak, Adrienne R., McQuaid, Ella, Jeong, Jacob, Albaum, Jonathan, Hrudka, E. M., Mulcahy, Owen T., Tanguma, Nolan C., Oishi-Holder, Sean, White, Zachary, Coe, Ryan W., Boyer, Christine, Chapman, Mitchell G., Fortino, Elise, Salgado, Jose A., Hellweg, Tim, Martinez, Hazelia K., Mitchell, Alexander J., Schubert, Stephanie H., Schumacher, Grace K, Tesdahl, Corey D, Uphoff, C. H., Vassilyev, Alexandr, Witkoff, Briahn, Wolle, Jackson R., Dice, Kenzie A., Behrer, Timothy A., Bowen, Troy, Campbell, Andrew J, Clarkson, Peter C, Duong, Tien Q., Hawat, Elijah, Lopez, Christian, Olson, Nathaniel P., Osborn, Matthew, Peou, Munisettha E., Vaver, Nicholas J., Husted, Troy, Kallemeyn, Nicolas Ian, Spangler, Ava A, Mccurry, Kyle, Schultze, Courtney, Troisi, Thomas, Thomas, Daniel, Ort, Althea E., Singh, Maya A., Soon, Caitlin, Patton, Catherine, Billman, Jayce A., Jarvis, Sam, Hitt, Travis, Masri, Mirna, Albalushi, Yusef J., Schofer, Matthew J, Linnane, Katherine B., Knott, Philip Whiting, Valencia, Whitney, Arias-Robles, Brian A., Ryder, Diana, Simone, Anna, Abrams, Jonathan M., Belknap, Annelene L., Rouse, Charlotte, Reynolds, Alexander, Petric, Romeo S. L., Gomez, Angel A., Meiselman-Ashen, Jonah B., Carey, Luke, Dias, John S., Fischer-White, Jules, Forbes, Aidan E., Galarraga, Gabriela, Kennedy, Forrest, Lawlor, Rian, Murphy, Maxwell J., Norris, Cooper, Quarderer, Josh, Waller, Caroline, Weber, Robert J., Gunderson, Nicole, Boyne, Tom, Gregory, Joshua A., Propper, Henry Austin, von Peccoz, Charles B. Beck, Branch, Donovan, Clarke, Evelyn, Cutler, Libby, Dabberdt, Frederick M., Das, Swagatam, Figueirinhas, John Alfred D., Fougere, Benjamin L., Roy, Zoe A., Zhao, Noah Y., Cox, Corben L., Barnhart, Logan D. W., Craig, Wilmsen B., Moll, Hayden, Pohle, Kyle, Mueller, Alexander, Smith, Elena K., Spicer, Benjamin C., Aycock, Matthew C., Bat-Ulzii, Batchimeg, Murphy, Madalyn C., Altokhais, Abdullah, Thornally, Noah R., Kleinhaus, Olivia R., Sarfaraz, Darian, Barnes, Grant M., Beard, Sara, Banda, David J, Davis, Emma A. B., Huebsch, Tyler J., Wagoner, Michaela, Griego, Justus, Hale, Jack J. Mc, Porter, Trevor J., Abrashoff, Riley, Phan, Denise M., Smith, Samantha M., Srivastava, Ashish, Schlenker, Jared A. W., Madsen, Kasey O., Hirschmann, Anna E., Rankin, Frederick C, Akbar, Zainab A., Blouin, Ethan, Coleman-Plante, Aislinn, Hintsa, Evan, Lookhoff, Emily, Amer, Hamzi, Deng, Tianyue, Dvorak, Peter, Minimo, Josh, Plummer, William C., Ton, Kelly, Solt, Lincoln, AlAbbas, Batool H., AlAwadhi, Areej A., Cooper, Nicholas M., Corbitt, Jessica S, Dunlap, Christian, Johnson, Owen, Malone, Ryan A., Tellez, Yesica, Wallace, Logan, Ta, Michael-Tan D., Wheeler, Nicola H., Ramirez, Ariana C., Huang, Shancheng, Mehidic, Amar, Christiansen, Katherine E, Desai, Om, Domke, Emerson N., Howell, Noah H., Allsbrook, Martin, Alnaji, Teeb, England, Colin, Siles, Nathan, Burton, Nicholas David, Cruse, Zoe, Gilmartin, Dalton, Kim, Brian T., Hattendorf, Elsie, Buhamad, Maryam, Gayou, Lily, Seglem, Kasper, Alkhezzi, Tameem, Hicks, Imari R., Fife, Ryann, Pelster, Lily M., Fix, Alexander, Sur, Sohan N., Truong, Joshua K., Kubiak, Bartlomiej, Bondar, Matthew, Shi, Kyle Z., Johnston, Julia, Acevedo, Andres B., Lee, Junwon, Solorio, William J., Johnston, Braedon Y., McCormick, Tyler, Olguin, Nicholas, Pastor, Paige J., Wilson, Evan M., Trunko, Benjamin L., Sjoroos, Chris, Adams, Kalvyn N, Bell, Aislyn, Brumage-Heller, Grant, Canales, Braden P., Chiles, Bradyn, Driscoll, Kailer H., Hill, Hallie, Isert, Samuel A., Ketterer, Marilyn, Kim, Matthew M., Mewhirter, William J., Phillips, Lance, Phommatha, Krista, Quinn, Megan S., Reddy, Brooklyn J., Rippel, Matthew, Russell, Bowman, Williams, Sajan, Pixley, Andrew M., Gapin, Keala C., Peterson, B., Ruprecht, Collin, Hardie, Isabelle, Li, Isaac, Erickson, Abbey, Gersabeck, Clint, Gopalani, Mariam, Allanqawi, Nasser, Burton, Taylor, Cahn, Jackson R., Conti, Reese, White, Oliver S., Rojec, Stewart, Hogen, Blake A., Swartz, Jason R., Dick, R., Battist, Lexi, Dunn, Gabrielle M., Gasser, Rachel, Logan, Timothy W., Sinkovic, Madeline, Schaller, Marcus T., Heintz, Danielle A., Enrich, Andrew, Sanchez, Ethan S., Perez, Freddy, Flores, Fernando, Kapla, Shaun D., Shockley, Michael C., Phillips, Justin, Rumley, Madigan, Daboub, Johnston, Karsh, Brennan J., Linders, Bridget, Chen, Sam, Do, Helen C., Avula, Abhinav, French, James M., Bertuccio, Chrisanna, Hand, Tyler, Lee, Adrianna J., Neeland, Brenna K, Salazar, Violeta, Andrew, Carter, Barmore, Abby, Beatty, Thomas, Alonzi, Nicholas, Brown, Ryan, Chandler, Olivia M., Collier, Curran, Current, Hayden, Delasantos, Megan E., Bonilla, Alberto Espinosa de los Monteros, Fowler, Alexandra A., Geneser, Julianne R., Gentry, Eleanor, Gustavsson, E. R., Hansson, Jonathan, Hao, Tony Yunfei, Herrington, Robert N., Kelly, James, Kelly, Teagan, Kennedy, Abigail, Marquez, Mathew J., Meillon, Stella, Palmgren, Madeleine L., Pesce, Anneliese, Ranjan, Anurag, Robertson, Samuel M., Smith, Percy, Smith, Trevor J, Soby, Daniel A., Stratton, Grant L., Thielmann, Quinn N., Toups, Malena C., Veta, Jenna S., Young, Trenton J., Maly, Blake, Manzanares, Xander R., Beijer, Joshua, George, Jacob D., Mills, Dylan P., Ziebold, Josh J, Chambers, Paige, Montoya, Michael, Cheang, Nathan M., Anderson, Hunter J., Duncan, Sheridan J., Ehrlich, Lauren, Hudson, Nathan C., Kiechlin, Jack L., Koch, Will, Lee, Justin, Menassa, Dominic, Oakes, S. H., Petersen, Audrey J., Bunsow, J. R. Ramirez, Bay, Joshua, Ramirez, Sacha, Fenwick, Logan D., Boyle, Aidan P., Hibbard, Lea Pearl, Haubrich, Calder, Sherry, Daniel P., Jenkins, Josh, Furney, Sebastian, Velamala, Anjali A., Krueger, Davis J., Thompson, William N., Chhetri, Jenisha, Lee, Alexis Ying-Shan, Ray, Mia G. V., Recchia, John C., Lengerich, Dylan, Taulman, Kyle, Romero, Andres C., Steward, Ellie N., Russell, Sloan, Hardwick, Dillon F., Wootten, Katelynn, Nguyen, Valerie A., Quispe, Devon, Ragsdale, Cameron, Young, Isabel, Atchley-Rivers, N. S., Stribling, Jordin L., Gentile, Julia G, Boeyink, Taylor A., Kwiatkowski, Daniel, Dupeyron, Tomi Oshima, Crews, Anastasia, Shuttleworth, Mitchell, Dresdner, Danielle C., Flackett, Lydia, Haratsaris, Nicholas, Linger, Morgan I, Misener, Jay H., Patti, Samuel, Pine, Tawanchai P., Marikar, Nasreen, Matessi, Giorgio, Routledge, Allie C., Alkaabi, Suhail, Bartman, Jessica L., Bisacca, Gabrielle E., Busch, Celeste, Edwards, Bree, Staudenmier, Caitlyn, Starling, Travis, McVey, Caden, Montano, Maximus, Contizano, Charles J., Taylor, Eleanor, McIntyre, James K., Victory, Andrew, McCammon, Glen S., Kimlicko, Aspen, Sheldrake, Tucker, Shelchuk, Grace, Von Reich, Ferin J., Hicks, Andrew J., O'neill, Ian, Rossman, Beth, Taylor, Liam C., MacDonald, William, Becker, Simone E., Han, Soonhee, O'Sullivan, Cian, Wilcove, Isaac, Brennan, David J., Hanley, Luke C., Hull, Owen, Wilson, Timothy R., Kalmus, Madison H., Berv, Owen A., Harris, Logan Swous, Doan, Chris H, Londres, Nathan, Parulekar, Anish, Adam, Megan M., Angwin, Abigail, Cabbage, Carter C., Colleran, Zachary, Pietras, Alex, Seux, Octave, Oros, Ryan, Wilkinson, Blake C., Nguyen, Khoa D, Trank-Greene, Maedee, Barone, Kevin M., Snyder, G. L., Biehle, Samuel J, Billig, Brennen, Almquist, Justin Thomas, Dixon, Alyssa M., Erickson, Benjamin, Evans, Nathan, Genne, SL, Kelly, Christopher M, Marcus, Serafima M., Ogle, Caleb, Patel, Akhil, Vendetti, Evan, Courtney, Olivia, Deel, Sean, Del Foco, Leonardo, Gjini, Michael, Haines, Jessica, Hoff, Isabelle J., Jones, M. R., Killian, Dominic, Kuehl, Kirsten, Kuester, Chrisanne, Lantz, Maxwell B., Lee, Christian J, Mauer, Graham, McKemey, Finbar K., Millican, Sarah J., Rosasco, Ryan, Stewart, T. C., VanEtten, Eleanor, Derwin, Zachary, Serio, Lauren, Sickler, Molly G., Blake, Cassidy A., Patel, Neil S., Fox, Margaret, Gray, Michael J, Ziegler, Lucas J., Kumar, Aman Priyadarshi, Polly, Madelyn, Mesgina, Sarah, McMorris, Zane, Griffin, Kyle J., Haile, L. N., Bassel, Claire, Dixon, Thomas J., Beattie, Ryan, Houck, Timothy J, Rodgers, Maeve, Trofino, Tyson R., Lukianow, Dax, Smart, Korben, Hall, Jacqueline L., Bone, Lauren, Baldwin, James O., Doane, Connor, Almohsen, Yousef A., Stamos, Emily, Acha, Iker, Kim, Jake, Samour II, Antonio E., Chavali, S., Kanokthippayakun, Jeerakit, Gotlib, Nicholas, Murphy, Ryan C., Archibald, Jack. W., Brimhall, Alexander J, Boyer, Aidan, Chapman, Logan T., Chadda, Shivank, Sibrell, Lisa, Vallery, Mia M., Conroy, Thomas C., Pan, Luke J., Balajonda, Brian, Fuhrman, Bethany E. S., Alkubaisi, Mohamed, Engelstad, Jacob, Dodrill, Joshua, Fuchs, Calvin R., Bullard-Connor, Gigi, Alhuseini, Isehaq, Zygmunt, James C., Sipowicz, Leo, Hayrynen, Griffin A., McGill, Riley M., Keating, Caden J., Hart, Omer, Cyr, Aidan St., Steinsberger, Christopher H., Thoman, Gerig, Wood, Travis M., Ingram, Julia A., Dominguez, J., Georgiades, Nathaniel James, Johnson, Matthew, Johnson, Sawyer, Pedersen, Alexander J., Ralapanawe, Anoush K, Thomas, Jeffrey J., Sato, Ginn A., Reynolds, Hope, Nasser, Liebe, Mizzi, Alexander Z., Damgaard, Olivia, Baflah, Abdulrahman A., Liu, Steven Y., Salindeho, Adam D., Norden, Kelso, Gearhart, Emily E., Krajnak, Zack, Szeremeta, Philip, Amos, Meggan, Shin, Kyungeun, Muckenthaler, Brandon A., Medialdea, Melissa, Beach, Simone, Wilson, Connor B., Adams, Elena R, Aldhamen, Ahmed, Harris, Coyle M., Hesse, Troy M., Golding, Nathan T., Larter, Zachary, Hernandez, Angel, Morales, Genaro, Traxler, Robert B., Alosaimi, Meshal, Fitton, Aidan F., Aaron, James Holland, Lee, Nathaniel F., Liao, Ryan Z., Chen, Judy, French, Katherine V., Loring, Justin, Colter, Aurora, McConvey, Rowan, Colozzi, Michael, Vann, John D., Scheck, Benjamin T., Weigand, Anthony A, Alhabeeb, Abdulelah, Idoine, Yolande, Woodard, Aiden L., Medellin, Mateo M., Ratajczyk, Nicholas O, Tobin, Darien P., Collins, Jack C., Horning, Thomas M., Pellatz, Nick, Pitten, John, Lordi, Noah, Patterson, Alyx, Hoang, Thi D, Zimmermann, Ingrid H, Wang, Hongda, Steckhahn, Daniel, Aradhya, Arvind J., Oliver, Kristin A., Cai, Yijian, Wang, Chaoran, Yegovtsev, Nikolay, Wu, Mengyu, Ganesan, Koushik, Osborne, Andrew, Wickenden, Evan, Meyer, Josephine C., Chaparro, David, Visal, Aseem, Liu, Haixin, Menon, Thanmay S., Jin, Yan, Wilson, John, Erikson, James W., Luo, Zheng, Shitara, Nanako, Nelson, Emma E, Geerdts, T. R., Ortiz, Jorge L Ramirez, and Lewandowski, H. J.

Subjects
Astrophysics - Solar and Stellar Astrophysics
Abstract

Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counter-intuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfv\'en waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, $\alpha=2$ as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed $>$600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: pre-flare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that $\alpha = 1.63 \pm 0.03$. This is below the critical threshold, suggesting that Alfv\'en waves are an important driver of coronal heating., Comment: 1,002 authors, 14 pages, 4 figures, 3 tables, published by The Astrophysical Journal on 2023-05-09, volume 948, page 71

Published
2023
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30. Multi‐scale, domain knowledge‐guided attention + random forest: a two‐stage deep learning‐based multi‐scale guided attention models to diagnose idiopathic pulmonary fibrosis from computed tomography images

Author

Yu, Wenxi, Zhou, Hua, Choi, Youngwon, Goldin, Jonathan G, Teng, Pangyu, Wong, Weng Kee, McNitt‐Gray, Michael F, Brown, Matthew S, and Kim, Grace Hyun J

Subjects
Medical and Biological Physics, Engineering, Physical Sciences, Biomedical Engineering, Bioengineering, Lung, Rare Diseases, Biomedical Imaging, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Autoimmune Disease, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Humans, Aged, Random Forest, Deep Learning, Idiopathic Pulmonary Fibrosis, Lung Diseases, Interstitial, Tomography, X-Ray Computed, Retrospective Studies, attention models, computed tomography, deep learning, domain knowledge, idiopathic pulmonary fibrosis, machine learning, medical imaging, Other Physical Sciences, Oncology and Carcinogenesis, Nuclear Medicine & Medical Imaging, Biomedical engineering, Medical and biological physics
Abstract

BackgroundIdiopathic pulmonary fibrosis (IPF) is a progressive, irreversible, and usually fatal lung disease of unknown reasons, generally affecting the elderly population. Early diagnosis of IPF is crucial for triaging patients' treatment planning into anti-fibrotic treatment or treatments for other causes of pulmonary fibrosis. However, current IPF diagnosis workflow is complicated and time-consuming, which involves collaborative efforts from radiologists, pathologists, and clinicians and it is largely subject to inter-observer variability.PurposeThe purpose of this work is to develop a deep learning-based automated system that can diagnose subjects with IPF among subjects with interstitial lung disease (ILD) using an axial chest computed tomography (CT) scan. This work can potentially enable timely diagnosis decisions and reduce inter-observer variability.MethodsOur dataset contains CT scans from 349 IPF patients and 529 non-IPF ILD patients. We used 80% of the dataset for training and validation purposes and 20% as the holdout test set. We proposed a two-stage model: at stage one, we built a multi-scale, domain knowledge-guided attention model (MSGA) that encouraged the model to focus on specific areas of interest to enhance model explainability, including both high- and medium-resolution attentions; at stage two, we collected the output from MSGA and constructed a random forest (RF) classifier for patient-level diagnosis, to further boost model accuracy. RF classifier is utilized as a final decision stage since it is interpretable, computationally fast, and can handle correlated variables. Model utility was examined by (1) accuracy, represented by the area under the receiver operating characteristic curve (AUC) with standard deviation (SD), and (2) explainability, illustrated by the visual examination of the estimated attention maps which showed the important areas for model diagnostics.ResultsDuring the training and validation stage, we observe that when we provide no guidance from domain knowledge, the IPF diagnosis model reaches acceptable performance (AUC±SD = 0.93±0.07), but lacks explainability; when including only guided high- or medium-resolution attention, the learned attention maps are not satisfactory; when including both high- and medium-resolution attention, under certain hyperparameter settings, the model reaches the highest AUC among all experiments (AUC±SD = 0.99±0.01) and the estimated attention maps concentrate on the regions of interests for this task. Three best-performing hyperparameter selections according to MSGA were applied to the holdout test set and reached comparable model performance to that of the validation set.ConclusionsOur results suggest that, for a task with only scan-level labels available, MSGA+RF can utilize the population-level domain knowledge to guide the training of the network, which increases both model accuracy and explainability.

Published
2023

31. An analysis of the regional heterogeneity in tissue elasticity in lung cancer patients with COPD

Author

Lauria, Michael, Stiehl, Bradley, Santhanam, Anand, O’Connell, Dylan, Naumann, Louise, McNitt-Gray, Michael, Raldow, Ann, Goldin, Jonathan, Barjaktarevic, Igor, and Low, Daniel A

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Clinical Research, Lung Cancer, Lung, Women's Health, Cancer, Bioengineering, Chronic Obstructive Pulmonary Disease, Biomedical Imaging, 4.1 Discovery and preclinical testing of markers and technologies, COPD, elasticity, lung heterogeneity, biomechanical properties, function sparing treatment planning, Biomedical and clinical sciences, Health sciences
Abstract

PurposeRecent advancements in obtaining image-based biomarkers from CT images have enabled lung function characterization, which could aid in lung interventional planning. However, the regional heterogeneity in these biomarkers has not been well documented, yet it is critical to several procedures for lung cancer and COPD. The purpose of this paper is to analyze the interlobar and intralobar heterogeneity of tissue elasticity and study their relationship with COPD severity.MethodsWe retrospectively analyzed a set of 23 lung cancer patients for this study, 14 of whom had COPD. For each patient, we employed a 5DCT scanning protocol to obtain end-exhalation and end-inhalation images and semi-automatically segmented the lobes. We calculated tissue elasticity using a biomechanical property estimation model. To obtain a measure of lobar elasticity, we calculated the mean of the voxel-wise elasticity values within each lobe. To analyze interlobar heterogeneity, we defined an index that represented the properties of the least elastic lobe as compared to the rest of the lobes, termed the Elasticity Heterogeneity Index (EHI). An index of 0 indicated total homogeneity, and higher indices indicated higher heterogeneity. Additionally, we measured intralobar heterogeneity by calculating the coefficient of variation of elasticity within each lobe.ResultsThe mean EHI was 0.223 ± 0.183. The mean coefficient of variation of the elasticity distributions was 51.1% ± 16.6%. For mild COPD patients, the interlobar heterogeneity was low compared to the other categories. For moderate-to-severe COPD patients, the interlobar and intralobar heterogeneities were highest, showing significant differences from the other groups.ConclusionWe observed a high level of lung tissue heterogeneity to occur between and within the lobes in all COPD severity cases, especially in moderate-to-severe cases. Heterogeneity results demonstrate the value of a regional, function-guided approach like elasticity for procedures such as surgical decision making and treatment planning.

Published
2023

33. Diagnosis and monitoring of systemic sclerosis-associated interstitial lung disease using high-resolution computed tomography.

Author

Khanna, Dinesh, Distler, Oliver, Cottin, Vincent, Brown, Kevin K, Chung, Lorinda, Goldin, Jonathan G, Matteson, Eric L, Kazerooni, Ella A, Walsh, Simon Lf, McNitt-Gray, Michael, and Maher, Toby M

Subjects
Systemic sclerosis, high-resolution computed tomography, imaging, interstitial lung disease, progressive fibrosing, radiation, Autoimmune Disease, Lung Cancer, Neurodegenerative, Lung, Brain Disorders, Rare Diseases, Biomedical Imaging, Bioengineering, Clinical Research, Prevention, Cancer, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Respiratory
Abstract

Patients with systemic sclerosis are at high risk of developing systemic sclerosis-associated interstitial lung disease. Symptoms and outcomes of systemic sclerosis-associated interstitial lung disease range from subclinical lung involvement to respiratory failure and death. Early and accurate diagnosis of systemic sclerosis-associated interstitial lung disease is therefore important to enable appropriate intervention. The most sensitive and specific way to diagnose systemic sclerosis-associated interstitial lung disease is by high-resolution computed tomography, and experts recommend that high-resolution computed tomography should be performed in all patients with systemic sclerosis at the time of initial diagnosis. In addition to being an important screening and diagnostic tool, high-resolution computed tomography can be used to evaluate disease extent in systemic sclerosis-associated interstitial lung disease and may be helpful in assessing prognosis in some patients. Currently, there is no consensus with regards to frequency and scanning intervals in patients at risk of interstitial lung disease development and/or progression. However, expert guidance does suggest that frequency of screening using high-resolution computed tomography should be guided by risk of developing interstitial lung disease. Most experienced clinicians would not repeat high-resolution computed tomography more than once a year or every other year for the first few years unless symptoms arose. Several computed tomography techniques have been developed in recent years that are suitable for regular monitoring, including low-radiation protocols, which, together with other technologies, such as lung ultrasound and magnetic resonance imaging, may further assist in the evaluation and monitoring of patients with systemic sclerosis-associated interstitial lung disease. A video abstract to accompany this article is available at: https://www.globalmedcomms.com/respiratory/Khanna/HRCTinSScILD.

Published
2022

35. Complications, Visual Acuity, and Refractive Error 3 Years after Secondary Intraocular Lens Implantation for Pediatric Aphakia

Author

Freedman, Sharon F., Wallace, David K., Enyedi, Laura B., Prakalapakorn, Sasapin, Jones, Sarah K., Hug, Denise, Stahl, Erin D., Dent, Rebecca J., Kong, Lingkun, Wang, Serena, Gallerson, Bryan K., Hutchinson, Amy K., Lenhart, Phoebe, Brower, Judy, Morrison, David G., Ruark, Scott T., Mets-Halgrimson, Rebecca, Yoon, Hawke, Ralay-Ranaivo, Hantamalala, Hamidullah, Aaliyah, Areaux, Raymond, Anderson, Jill S., Holleschau, Ann M., Superstein, Rosanne, Belanger, Caroline, Fallaha, Nicole, Hamel, Patrick, Thibeault, Maryse, Tamkins, Susanna M., Chang, Ta, Park, Hee-Jung S., Trumler, Anya A., Liu, Xiaonong, Astle, William F., Sanders, Emi N., Traboulsi, Elias, Ghasia, Fatema, McOwen, Diana C., Gray, Michael E., Yang, Michael B., Bowman, Corey S., Galvin, Jennifer, Therriault, Margaret, Smith, Heather, Whitaker, Michele E., Orge, Faruk, Grigorian, Adriana P., Baird, Alicia M., Strominger, Mitchell B., Chen, Vicki, Klein, Shelley, Kemmer, Jacquelyn D., Neiman, Alexandra E., Mendoza, Myra N., Frohwein, Jill J., Bremer, Don, Cassady, Cybil, Golden, Richard, Jordan, Catherine, Rogers, David, Oravec, Sara A., Yanovitch, Tammy L., Lunsford, Keven, Nye, Christina, Shea, Caroline, Stillman, SueAnn M., LaRoche, G. Robert, Van Iderstine, Stephen C., Robertson, Elisa, Cruz, Oscar A., Ghadban, Rafif, Govreau, Dawn, Larson, Scott A., Longmuir, Susannah, Shan, Xiaoyan, Clarke, Michael P., Taylor, Kate, Powell, Christine, Hammond, Benjamin P., Gearinger, Matthew D., Czubinski, Andrea, Hendricks, Dorothy H., Jin, Jing, Salvin, Jonathan H., Fisher, Alicia, Lee, Katherine A., Brooks, Daniel, Schweinler, Bonita R., Sala, Nicholas A., Sala, Allyson M., Summers, Allison I., Karr, Daniel J., Wilson, Lorri B., Rauch, Paula K., O'Hara, Mary, Gandhi, Nandini, Hashmi, Tania, Colburn, Jeffrey, Dittman, Eileen, Whitfill, Charles R., Wheeler, Amy M., McCourt, Emily A., Singh, Jasleen, Welnick, Nanastasia, Azar, Nathalie F., Baker, Joseph, Droste, Patrick J., Peters, Robert J., Hilbrands, Jan, Pineles, Stacy L., Bernardo, Marianne J., Peterson, Edward, Peterson, Charla H., Kumar, Kartik, Melese, Ephrem, Lingua, Robert, Grijalva, Jeff, Crouch, Earl R., jr., Crouch, Earl R., III, Ventura, Gaylord, Anninger, William, Benson, Shawn L., Karp, Karen A., Smith, Jordana M., Brickman-Kelleher, Jill, Ticho, Benjamin H., Khammar, Alexander J., Clausius, Deborah A., Guo, Suquin, Suh, Donny, Chamberlain, Carolyn, Schloff, Susan, Madigan, William P., Burkman, Donna, Christiansen, Stephen P., Ramsey, Jean E., McConnell, Kate H., Friedman, Ilana, Rosado, Jose, Sauberan, Donald P., Hemberger, Jody C., Davis, Patricia L., Rudaitis, Indre, Lowery, Robert S., Cupit, Shawn, Bothun, Erick D., Mohney, Brian G., Wernimont, Suzanne M., Neilsen, Rebecca A., Herlihy, Erin P., Baran, Francine, Gladstone, Amy, Smith, Justin, Mellott, Mei, Kieser, Troy, Erzurum, S. Ayse, Colon, Beth, Shah, Birva, Quebbemann, Micaela, Beck, Roy W., Austin, Darrell S., Boyle, Nicole M., Conner, Courtney L., Chandler, Danielle L., Donahue, Quayleen, Fimbel, Brooke P., Robinson, Julianne L., Hercinovic, Amra, Hoepner, James E., Kaplon, Joseph D., Henderson, Robert J., Melia, B. Michele, Ortiz, Gillaine, Woodard, Victoria C., Stutz, Kathleen M., Sutherland, Desirae R., Wu, Rui, Everett, Donald F., Diener-West, Marie, Baker, John D., Davis, Barry, Phelps, Dale L., Poff, Stephen W., Saunders, Richard A., Tychsen, Lawrence, Bradfield, Yasmin S., Foster, Nicole C., Plager, David A., Salchow, Daniel J., Birch, Eileen E., Manny, Ruth E., Silver, Jayne L., Weise, Katherine K., Verderber, Lisa C., Repka, Michael X., Dean, Trevano W., Kraker, Raymond T., Li, Zhuokai, Yen, Kimberly G., de Alba Campomanes, Alejandra G., Young, Marielle P., Rahmani, Bahram, Haider, Kathryn M., Whitehead, George F., Lambert, Scott R., Kurup, Sudhi P., Kraus, Courtney L., Cotter, Susan A., Holmes, Jonathan M., Hatt, Sarah R., and Traboulsi, Elias I.

Published
2024
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37. Local linearity analysis of deep learning CT denoising algorithms

Author

Li, Junyuan, Wang, Wenying, Tivnan, Matthew, Sulam, Jeremias, Prince, Jerry L, McNitt-Gray, Michael, Stayman, J Webster, and Gang, Grace J

Subjects
Engineering, Communications Engineering, Electronics, Sensors and Digital Hardware, Physical Sciences, Atomic, Molecular and Optical Physics, Bioengineering, Communications engineering, Electronics, sensors and digital hardware, Atomic, molecular and optical physics
Abstract

The rapid development of deep-learning methods in medical imaging has called for an analysis method suitable for non-linear and data-dependent algorithms. In this work, we investigate a local linearity analysis where a complex neural network can be represented as piecewise linear systems. We recognize that a large number of neural networks consists of alternating linear layers and rectified linear unit (ReLU) activations, and are therefore strictly piecewise linear. We investigated the extent of these locally linear regions by gradually adding perturbations to an operating point. For this work, we explored perturbations based on image features of interest, including lesion contrast, background, and additive noise. We then developed strategies to extend these strictly locally linear regions to include neighboring linear regions with similar gradients. Using these approximately linear regions, we applied singular value decomposition (SVD) analysis to each local linear system to investigate and explain the overall nonlinear and data-dependent behaviors of neural networks. The analysis was applied to an example CT denoising algorithm trained on thorax CT scans. We observed that the strictly local linear regions are highly sensitive to small signal perturbations. Over a range of lesion contrast from 0.007 to 0.04 mm-1, there is a total of 33992 linear regions. The Jacobians are also shift-variant. However, the Jacobians of neighboring linear regions are very similar. By combining linear regions with similar Jacobians, we narrowed down the number of approximately linear regions to four over lesion contrast from 0.001 to 0.08 mm-1. The SVD analysis to different linear regions revealed denoising behavior that is highly dependent on the background intensity. Analysis further identified greater amount of noise reduction in uniform regions compared to lesion edges. In summary, the local linearity analysis framework we proposed has the potential for us to better characterize and interpret the non-linear and data-dependent behaviors of neural networks.

Published
2022

39. Making existing software quantum safe: a case study on IBM Db2

Author

Zhang, Lei, Miranskyy, Andriy, Rjaibi, Walid, Stager, Greg, Gray, Michael, and Peck, John

Subjects
Computer Science - Software Engineering, Computer Science - Cryptography and Security, Computer Science - Emerging Technologies
Abstract

The software engineering community is facing challenges from quantum computers (QCs). In the era of quantum computing, Shor's algorithm running on QCs can break asymmetric encryption algorithms that classical computers practically cannot. Though the exact date when QCs will become "dangerous" for practical problems is unknown, the consensus is that this future is near. Thus, the software engineering community needs to start making software ready for quantum attacks and ensure quantum safety proactively. We argue that the problem of evolving existing software to quantum-safe software is very similar to the Y2K bug. Thus, we leverage some best practices from the Y2K bug and propose our roadmap, called 7E, which gives developers a structured way to prepare for quantum attacks. It is intended to help developers start planning for the creation of new software and the evolution of cryptography in existing software. In this paper, we use a case study to validate the viability of 7E. Our software under study is the IBM Db2 database system. We upgrade the current cryptographic schemes to post-quantum cryptographic ones (using Kyber and Dilithium schemes) and report our findings and lessons learned. We show that the 7E roadmap effectively plans the evolution of existing software security features towards quantum safety, but it does require minor revisions. We incorporate our experience with IBM Db2 into the revised 7E roadmap. The U.S. Department of Commerce's National Institute of Standards and Technology is finalizing the post-quantum cryptographic standard. The software engineering community needs to start getting prepared for the quantum advantage era. We hope that our experiential study with IBM Db2 and the 7E roadmap will help the community prepare existing software for quantum attacks in a structured manner., Comment: 25 pages, 4 figures

Published
2021
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41. Lipidomic Risk Score to Enhance Cardiovascular Risk Stratification for Primary Prevention

Author

Wu, Jingqin, Giles, Corey, Dakic, Aleksandar, Beyene, Habtamu B., Huynh, Kevin, Wang, Tingting, Meikle, Thomas, Olshansky, Gavriel, Salim, Agus, Duong, Thy, Watts, Gerald F., Hung, Joseph, Hui, Jennie, Cadby, Gemma, Beilby, John, Blangero, John, Moses, Eric K., Shaw, Jonathan E., Magliano, Dianna J., Zhu, Dantong, Yang, Jean Y., Grieve, Stuart M., Wilson, Andrew, Chow, Clara K., Vernon, Stephen T., Gray, Michael P., Figtree, Gemma A., Carrington, Melinda J., Inouye, Mike, Marwick, Thomas H., and Meikle, Peter J.

Published
2024
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45. Recent expansion of metabolic versatility in Diplonema papillatum, the model species of a highly speciose group of marine eukaryotes

Author

Valach, Matus, Moreira, Sandrine, Petitjean, Celine, Benz, Corinna, Butenko, Anzhelika, Flegontova, Olga, Nenarokova, Anna, Prokopchuk, Galina, Batstone, Tom, Lapébie, Pascal, Lemogo, Lionnel, Sarrasin, Matt, Stretenowich, Paul, Tripathi, Pragya, Yazaki, Euki, Nara, Takeshi, Henrissat, Bernard, Lang, B. Franz, Gray, Michael W., Williams, Tom A., Lukeš, Julius, and Burger, Gertraud

Published
2023
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47. Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge

Author

Balagurunathan, Yoganand, Beers, Andrew, Mcnitt-Gray, Michael, Hadjiiski, Lubomir, Napel, Sandy, Goldgof, Dmitry, Perez, Gustavo, Arbelaez, Pablo, Mehrtash, Alireza, Kapur, Tina, Yang, Ehwa, Moon, Jung Won, Perez, Gabriel Bernardino, Delgado-Gonzalo, Ricard, Farhangi, M Mehdi, Amini, Amir A, Ni, Renkun, Feng, Xue, Bagari, Aditya, Vaidhya, Kiran, Veasey, Benjamin, Safta, Wiem, Frigui, Hichem, Enguehard, Joseph, Gholipour, Ali, Castillo, Laura Silvana, Daza, Laura Alexandra, Pinsky, Paul, Kalpathy-Cramer, Jayashree, and Farahani, Keyvan

Subjects
Information and Computing Sciences, Engineering, Bioengineering, Cancer, Lung Cancer, Lung, Clinical Research, Biomedical Imaging, Good Health and Well Being, Algorithms, Humans, Lung Neoplasms, ROC Curve, Solitary Pulmonary Nodule, Tomography, X-Ray Computed, Training, Deep learning, Lung cancer, Computed tomography, Biomedical imaging, Pathology, nodules challenge, ISBI 2018, indeterminate pulmonary nodules, cancer detection in longitudinal CT, NLST, computed comography, deep learning methods in lung CT, Nuclear Medicine & Medical Imaging, Information and computing sciences
Abstract

Lung cancer is by far the leading cause of cancer death in the US. Recent studies have demonstrated the effectiveness of screening using low dose CT (LDCT) in reducing lung cancer related mortality. While lung nodules are detected with a high rate of sensitivity, this exam has a low specificity rate and it is still difficult to separate benign and malignant lesions. The ISBI 2018 Lung Nodule Malignancy Prediction Challenge, developed by a team from the Quantitative Imaging Network of the National Cancer Institute, was focused on the prediction of lung nodule malignancy from two sequential LDCT screening exams using automated (non-manual) algorithms. We curated a cohort of 100 subjects who participated in the National Lung Screening Trial and had established pathological diagnoses. Data from 30 subjects were randomly selected for training and the remaining was used for testing. Participants were evaluated based on the area under the receiver operating characteristic curve (AUC) of nodule-wise malignancy scores generated by their algorithms on the test set. The challenge had 17 participants, with 11 teams submitting reports with method description, mandated by the challenge rules. Participants used quantitative methods, resulting in a reporting test AUC ranging from 0.698 to 0.913. The top five contestants used deep learning approaches, reporting an AUC between 0.87 - 0.91. The team's predictor did not achieve significant differences from each other nor from a volume change estimate (p =.05 with Bonferroni-Holm's correction).

Published
2021

50. Eight-Year Outcomes of Bilateral Lateral Rectus Recessions versus Unilateral Recession-Resection in Childhood Basic-Type Intermittent Exotropia

Author

Miller, Aaron M., Olvera, Maria N., Alexander, Monsey L., Curtin, Kathleen Mary, Dillon, Angela C., Gray, Carole L., Jackson, Jorie L., Qadir, Ximena V., Ramos, Cynthia R., Paysse, Evelyn A., Coats, David K., Yen, Kimberly G., Romany, Gihan, Homann, Melynda J., Kong, Lingkun, Law, Christine, Churchill, Sarah, MacSween, Lesley E., Hoover, Darren L., Huston, Pamela A., Racan, Pamela M., Soros, Kari E., Sala, Nicholas A., Sala, Nicholas Anthony, Johnson, Catherine, Sala, Allyson, Zeto, V. Lori, Donahue, Sean P., Wilkins, Carsyn Saige, Biernacki, Ronald J., Campbell, Megan K., Fraine, Lisa A., Ruark, Scott T., Crouch, Eric, Crouch, Earl R., Jr., Ventura, Gaylord G., Fritz, Carolina Andrea, Anderson, Jill S., Areaux, Raymond G., Jr, Holleschau, Ann M., Harder, Jessica Ann, May, Laura M., Merrill, Kim S., Schweigert, Anna I., Petersen, David B., Pickens, Tori S., McMurtrey, J. Ryan, Morrell, Beth A., Repka, Michael X., Liu, Xiaonong, Christoff, Alex, Silbert, David I., Modjesky, Heather, Woodall, Hayley L., Summers, Allison I., Kuo, Annie F., Wilson, Lorri B., Rauch, Paula, Lee, Jessy, Casey, Grant A., Narain, Srianna, Woodruff, Kevin, Ticho, Benjamin H., Clausius, Deborah A., Allen, Megan, Quebbemann, Micaela N., Shah, Birva K., Bothun, Erick D., Holmes, Jonathan M., Mohney, Brian G., Wernimont, Suzanne M., Czaplewski, Lindsay L., Eastman, Stacy L., Huisman, Jordan Joseph, Klaehn, Lindsay D., Kramer, Andrea M., Kroening, Rose M., Priebe, Debbie M., Wohlers, Moriah A., Jensen, Allison A., Flanagan, Maureen A., Tolbert, Tiffany Talia, Traboulsi, Elias, Ghasia, Fatema F., Meador, Angela M., McOwen, Diana Christine, Enyedi, Laura B., Jones, Sarah K., Kashyap, Namita, Loud, Rachel N., Waters, Amy L., Marsh, Justin D., Bond, Lezlie L., Ariss, Michelle M., Dent, Rebecca J., Phillips, Paul H., Lowery, Robert Scott, Haley, Wendy Jean, Brown, Shaina, Colon, Beth, Cupit, Shawn L., Holtorf, Hannah L., Sanders, Hayley Elizabeth, Bowsher, James D., Cheeseman, Edward W., Weas, Nikki M., Bradham, Carol U., Rahmani, Bahram, Ranaivo, Hantamalala Ralay, Cruz, Karla G., De Leon, Erika A., Klauer, Anthony Jeffrey, Tzanetakos, Vivian, McCoy Vrablec, Laura, Orge, Faruk H., Richards, Leslie, Baird, Alicia Marie, Glaser, Stephen R., Yost, Kasey L., Flores, Odalis R., Herlihy, Erin P., Taira, Alyssa, Alexander, Jessica, Gladstone, Amy, Kiens, Bridget Ann, Tews, Lyndsey A., Whitehead, George F., Shea, Caroline J., Stillman, SueAnn Marie, Nye, Christina N., Bartiss, Michael John, McGaw, Tennille F., Davis, Patricia L., Hulett, Katie R., Twite, Jacqueline, Bradfield, Yasmin S., Adler, Angela M., Anderson, Kristin A., Kraker, Raymond T., Beck, Roy W., Austin, Darrell S., Boyle, Nicole M., Chandler, Danielle L., Connelly, Patricia L., Conner, Courtney L., Donahue, Quayle, Fimbel, Brooke P., Henderson, Robert J., Hoepner, James E., Kaplon, Joseph D., Melia, B. Michele, Ortiz, Gillaine, Robinson, Julianne L., Stutz, Kathleen M., Sutherland, Desirae R., Toro, David O., Woodard, Victoria C., Wu, Rui, Cotter, Susan A., Birch, Eileen E., Christiansen, Stephen P., Hatt, Sarah R., Leske, David A., Melia, Michele, O’Hara, Mary, Pang, Yi, Romanchuck, Kenneth, Tamkins, Susanna M., Wallace, David K., Wheeler, David T., Bhatt, Amit, Chen, Angela M., Cheung, Nathan L., Cobb, Patricia, Colon, Beth J., Crouch, Eric R., Dean, Trevano W., Erzurum, S. Ayse, Esposito, Christina A., Fang, Caroline C., Gray, Michael E., Gunton, Kammi B., Hopkins, Kristine B., Jastrzembski, Benjamin G., Jenewein, Erin C., Jordan, Catherine O., Kraus, Courtney, Kurup, Sudhi P., Lazar, Elizabeth L., Li, Zhuokai, Lorenzana, Ingryd, McDowell, Paula S., Morrison, Ann M., Morrison, David G., Nylin, Elyse, Parker, Sue M., Patel, Reena, Plaumann, Maureen D., Pollack, Karen, Raghuram, Aparna, Retnasothie, Dashaini V., Roberts, Tawna L., Scheiman, Mitchell M., Shah, Veeral S., Superstein, Rosanne, Titelbaum, Jenna R., Vricella, Marilyn, Yamada, Tomohiko, Astle, William F., Christian, Melanie L., Everett, Donald F., Freedman, Sharon F., Good, William V., Lambert, Scott R., Lee, Katherine A., London, Richard, Manh, Vivian M., Manny, Ruth E., Pineles, Stacy L., Rogers, David L., Schweinler, Bonita R., Silver, Jayne L., Suh, Donny W., Verderber, Lisa C., Weise, Katherine K., Diener-West, Marie, Baker, John D., Davis, Barry, Higgins, Rosemary D., Poff, Stephen W., Saunders, Richard A., and Tychsen, Lawrence

Published
2024
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