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17,189 results on '"Thompson, John A."'

5. The Effect of Neoadjuvant Systemic Therapy on Surgical Outcomes After Lymph Node Dissections for Stage III Melanoma; An Australian Cohort

Author

Zijlker, Lisanne P., Chen, Henry, Spillane, Andrew J., Gonzalez, Maria, Pennington, Thomas E., Menzies, Alexander M., Lo, Serigne N., Ferguson, Peter, Rawson, Robert, Colebatch, Andrew J., Stretch, Jonathan R., Thompson, John F., Ch’ng, Sydney, Nieweg, Omgo, Shannon, Kerwin F., Long, Georgina V., Scolyer, Richard A., Saw, Robyn P. M., and van Akkooi, Alexander C. J.

Published
2024
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8. Data Formulator: AI-powered Concept-driven Visualization Authoring

Author

Wang, Chenglong, Thompson, John, and Lee, Bongshin

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence
Abstract

With most modern visualization tools, authors need to transform their data into tidy formats to create visualizations they want. Because this requires experience with programming or separate data processing tools, data transformation remains a barrier in visualization authoring. To address this challenge, we present a new visualization paradigm, concept binding, that separates high-level visualization intents and low-level data transformation steps, leveraging an AI agent. We realize this paradigm in Data Formulator, an interactive visualization authoring tool. With Data Formulator, authors first define data concepts they plan to visualize using natural languages or examples, and then bind them to visual channels. Data Formulator then dispatches its AI-agent to automatically transform the input data to surface these concepts and generate desired visualizations. When presenting the results (transformed table and output visualizations) from the AI agent, Data Formulator provides feedback to help authors inspect and understand them. A user study with 10 participants shows that participants could learn and use Data Formulator to create visualizations that involve challenging data transformations, and presents interesting future research directions.

Published
2023

9. WonderFlow: Narration-Centric Design of Animated Data Videos

Author

Wang, Yun, Shen, Leixian, You, Zhengxin, Shu, Xinhuan, Lee, Bongshin, Thompson, John, Zhang, Haidong, and Zhang, Dongmei

Subjects
Computer Science - Human-Computer Interaction
Abstract

Creating an animated data video enriched with audio narration takes a significant amount of time and effort and requires expertise. Users not only need to design complex animations, but also turn written text scripts into audio narrations and synchronize visual changes with the narrations. This paper presents WonderFlow, an interactive authoring tool, that facilitates narration-centric design of animated data videos. WonderFlow allows authors to easily specify a semantic link between text and the corresponding chart elements. Then it automatically generates audio narration by leveraging text-to-speech techniques and aligns the narration with an animation. WonderFlow provides a visualization structure-aware animation library designed to ease chart animation creation, enabling authors to apply pre-designed animation effects to common visualization components. It also allows authors to preview and iteratively refine their data videos in a unified system, without having to switch between different creation tools. To evaluate WonderFlow's effectiveness and usability, we created an example gallery and conducted a user study and expert interviews. The results demonstrated that WonderFlow is easy to use and simplifies the creation of data videos with narration-animation interplay., Comment: Accepted by TVCG

Published
2023

10. A consensus definition for lamellar macular hole

Author

Hubschman, Jean-Pierre, Govetto, Andrea, Spaide, Richard, Schumann, RG, Steel, David H, Figueroa, Marta, Sebag, J, Gaudric, Alain, Staurenghi, Giovanni, Haritoglou, Christos, Kazuaki, Kadonosono, Thompson, John, Chang, Stanley, Bottoni, Ferdinando, and Tadayoni, Ramin

Subjects
Retina, Macula, Clinical Sciences, Opthalmology and Optometry, Public Health and Health Services, Ophthalmology & Optometry, Clinical sciences, Ophthalmology and optometry
Abstract

Background: A consensus on an optical coherence tomography (OCT) definition of lamellar macular hole (LMH) and similar conditions is needed. Methods: The panel reviewed relevant peer-reviewed literature to reach a consensus on LMH definition and to differentiate LMH from other similar conditions.Results: The panel reached a consensus on the definition of 3 clinical entities: LMH, epiretinal membrane (ERM) foveoschisis and macular pseudohole (MPH). LMH definition is based on 3 mandatory criteria and 3 optional anatomical features. The 3 mandatory criteria are the presence of irregular foveal contour, the presence of a foveal cavity with undermined edges, and the apparent loss of foveal tissue. Optional anatomical features include the presence of epiretinal proliferation, the presence of a foveal bump and the disruption of the ellipsoid line. ERM foveoschisis definition is based on 2 mandatory criteria: the presence of ERM and the presence of schisis at the level of Henle’s fiber layer. Three optional anatomical features can also be present: the presence of microcystoid spaces in the inner nuclear layer (INL), an increase of retinal thickness, and the presence of retinal wrinkling. MPH definition is based on 3 mandatory criteria and 2 optional anatomical features. Mandatory criteria include the presence of a foveal sparing ERM, the presence of a steepened foveal profile and an increased central retinal thickness. Optional anatomical features are the presence of microcystoid spaces in the INL and a normal retinal thickness.Conclusions: The use of the proposed definitions may provide uniform language for clinicians and future research.

Published
2023

11. Mixed-type Distance Shrinkage and Selection for Clustering via Kernel Metric Learning

Author

Ghashti, Jesse S. and Thompson, John R. J.

Subjects
Computer Science - Machine Learning, Statistics - Computation, Statistics - Methodology, Statistics - Other Statistics, 62G07, 65D10, I.5.3, G.3, I.6.6
Abstract

Distance-based clustering and classification are widely used in various fields to group mixed numeric and categorical data. In many algorithms, a predefined distance measurement is used to cluster data points based on their dissimilarity. While there exist numerous distance-based measures for data with pure numerical attributes and several ordered and unordered categorical metrics, an efficient and accurate distance for mixed-type data that utilizes the continuous and discrete properties simulatenously is an open problem. Many metrics convert numerical attributes to categorical ones or vice versa. They handle the data points as a single attribute type or calculate a distance between each attribute separately and add them up. We propose a metric called KDSUM that uses mixed kernels to measure dissimilarity, with cross-validated optimal bandwidth selection. We demonstrate that KDSUM is a shrinkage method from existing mixed-type metrics to a uniform dissimilarity metric, and improves clustering accuracy when utilized in existing distance-based clustering algorithms on simulated and real-world datasets containing continuous-only, categorical-only, and mixed-type data., Comment: 38 pages, 3 tables, 8 figures

Published
2023

12. ASO Visual Abstract: The Effect of Neoadjuvant Systemic Therapy on Surgical Outcomes After Lymph Node Dissections for Stage III Melanoma: An Australian Cohort

Author

Zijlker, Lisanne P., Chen, Henry, Spillane, Andrew J., Gonzalez, Maria, Pennington, Thomas E., Menzies, Alexander M., Lo, Serigne N., Ferguson, Peter, Rawson, Robert, Colebatch, Andrew J., Stretch, Jonathan R., Thompson, John F., Ch’ng, Sydney, Nieweg, Omgo, Shannon, Kerwin F., Long, Georgina V., Scolyer, Richard A., Saw, Robyn P. M., and van Akkooi, Alexander C. J.

Published
2024
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16. Role of Concurrent Ultrasound Surveillance of Sentinel Node-Positive Node Fields in Melanoma Patients Having Routine Cross-Sectional Imaging

Author

Gjorup, Caroline A., Woodford, Rachel, Li, Isabel, Carlino, Matteo S., Ch’ng, Sydney, Chung, David, Hsiao, Edward, Lo, Serigne N., London, Kevin, Long, Georgina V., Menzies, Alexander M., Nieweg, Omgo E., Pennington, Thomas E., Rtshiladze, Michael A., Saw, Robyn P. M., Scolyer, Richard A., Shannon, Kerwin F., Spillane, Andrew J., Stretch, Jonathan R., Thompson, John F., Varey, Alexander H. R., and van Akkooi, Alexander C. J.

Published
2024
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17. On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds

Author

Wang, Cheng-Xiang, You, Xiaohu, Gao, Xiqi, Zhu, Xiuming, Li, Zixin, Zhang, Chuan, Wang, Haiming, Huang, Yongming, Chen, Yunfei, Haas, Harald, Thompson, John S., Larsson, Erik G., Di Renzo, Marco, Tong, Wen, Zhu, Peiying, Xuemin, Shen, Poor, H. Vincent, and Hanzo, Lajos

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed.

Published
2023

18. Channelformer: Attention based Neural Solution for Wireless Channel Estimation and Effective Online Training

Author

Luan, Dianxin and Thompson, John

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence
Abstract

In this paper, we propose an encoder-decoder neural architecture (called Channelformer) to achieve improved channel estimation for orthogonal frequency-division multiplexing (OFDM) waveforms in downlink scenarios. The self-attention mechanism is employed to achieve input precoding for the input features before processing them in the decoder. In particular, we implement multi-head attention in the encoder and a residual convolutional neural architecture as the decoder, respectively. We also employ a customized weight-level pruning to slim the trained neural network with a fine-tuning process, which reduces the computational complexity significantly to realize a low complexity and low latency solution. This enables reductions of up to 70\% in the parameters, while maintaining an almost identical performance compared with the complete Channelformer. We also propose an effective online training method based on the fifth generation (5G) new radio (NR) configuration for the modern communication systems, which only needs the available information at the receiver for online training. Using industrial standard channel models, the simulations of attention-based solutions show superior estimation performance compared with other candidate neural network methods for channel estimation., Comment: Accepted by IEEE Transactions on Wireless Communications

Published
2023

19. Achieving Robust Generalization for Wireless Channel Estimation Neural Networks by Designed Training Data

Author

Luan, Dianxin and Thompson, John

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence
Abstract

In this paper, we propose a method to design the training data that can support robust generalization of trained neural networks to unseen channels. The proposed design that improves the generalization is described and analysed. It avoids the requirement of online training for previously unseen channels, as this is a memory and processing intensive solution, especially for battery powered mobile terminals. To prove the validity of the proposed method, we use the channels modelled by different standards and fading modelling for simulation. We also use an attention-based structure and a convolutional neural network to evaluate the generalization results achieved. Simulation results show that the trained neural networks maintain almost identical performance on the unseen channels., Comment: Accepted by ICC 2023

Published
2023

22. Concurrent immune checkpoint inhibition and selective immunosuppressive therapy in patients with immune-related enterocolitis.

Author

Badran, Yousef R, Zou, Fangwen, Durbin, Sienna M, Dutra, Barbara E, Abu-Sbeih, Hamzah, Thomas, Anusha S, Altan, Mehmet, Thompson, John A, Qiao, Wei, Leet, Donna E, Lai, Po-Ying, Horick, Nora K, Postow, Michael A, Faleck, David M, Wang, Yinghong, and Dougan, Michael

Subjects
Humans, Enterocolitis, Retrospective Studies, Infliximab, Antineoplastic Agents, Immunological, Immune Checkpoint Inhibitors, Immunosuppression Therapy, Autoimmunity, Immunotherapy, Inflammation, Digestive Diseases, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals
Abstract

PurposeImmune checkpoint inhibitor (ICI) therapy is often suspended because of immune-related enterocolitis (irEC). We examined the effect of resumption of ICIs with or without concurrent selective immunosuppressive therapy (SIT) on rates of symptom recurrence and survival outcomes.MethodsThis retrospective, multicenter study examined patients who were treated with ICI and developed irEC requiring SIT (infliximab or vedolizumab) for initial symptom control or to facilitate steroid tapering between May 2015 and June 2020. After symptom resolution, patients were restarted either on ICI alone or on concurrent ICI and SIT at the discretion of the treating physicians. The associations between irEC recurrence and treatment group were assessed via univariate analyses and multivariate logistic regression. Cox proportional hazards model was used for survival analysis.ResultsOf the 138 included patients who required SIT for initial irEC symptom control, 61 (44.2%) patients resumed ICI without concurrent SIT (control group) and 77 (55.8%) patients resumed ICI therapy with concurrent SIT: 33 with infliximab and 44 with vedolizumab. After symptom resolution, patients in the control group were more commonly restarted on a different ICI regimen (65.6%) compared with those receiving SIT (31.2%) (p

Published
2023

24. DiSPATCHES FROM THE FAST TRACT

Author

Abrams, Sean, Epstein, Rachel, and Thompson, John

Subjects
Health
Abstract

QUARTER-LIFE CRISIS? NEVER HEARD OF IT. HERE, SiX GUYS WHO ACCOMPLiSHED SOME AMAZiNG #LiFEGOALS BEFORE TURNiNG 30 OPEN UP ABOUT HOW THEY DID iT, WHERE THEY GO FROM HERE, AND [...]

Published
2024

25. Student Understanding of the Sign of Negative Definite Integrals in Mathematics and Physics

Author

Bajracharya, Rabindra R., Sealey, Vicki L., and Thompson, John R.

Abstract

In a study of student understanding of negative definite integrals at two institutions, we administered a written survey and follow-up clinical interviews at one institution and found that "backward integrals", where the integral was taken from right to left on the x-axis, were the most difficult for students to interpret. We then conducted additional interviews focused on backward integrals at a second institution. Our analysis uses the concept image framework and a recent categorical framework for mathematical sense making. We found that students were most successful using the Fundamental Theorem of Calculus to determine the sign of an integral when a symbolic expression was provided. When considering a definite integral in a graphical context, students often had difficulty if they viewed the integral as a spatial area, stating that area must always be positive. Some students were able to conceptualize [delta]x or dx as a difference or change and thus a signed quantity; when these students were able to view the area as a sum of smaller pieces, they were more successful in justifying a negative backward integral. Students who used amounts or similar images often had difficulty making sense of the negative sign. Even more progress was made when students either invoked or were asked explicitly about a specific physical context to be represented by the backward integral, other than spatial area. The context provided a meaning to the difference represented by [delta]x or dx and thus to the sign of that difference and the definite integral.

Published
2023
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28. 6G Network AI Architecture for Everyone-Centric Customized Services

Author

Yang, Yang, Ma, Mulei, Wu, Hequan, Yu, Quan, Zhang, Ping, You, Xiaohu, Wu, Jianjun, Peng, Chenghui, Yum, Tak-Shing Peter, Shen, Sherman, Aghvami, Hamid, Li, Geoffrey Y, Wang, Jiangzhou, Liu, Guangyi, Gao, Peng, Tang, Xiongyan, Cao, Chang, Thompson, John, Wong, Kat-Kit, Chen, Shanzhi, Debbah, Merouane, Dustdar, Schahram, Eliassen, Frank, Chen, Tao, Duan, Xiangyang, Sun, Shaohui, Tao, Xiaofeng, Zhang, Qinyu, Huang, Jianwei, Cui, Shuguang, Zhang, Wenjun, Li, Jie, Gao, Yue, Zhang, Honggang, Chen, Xu, Ge, Xiaohu, Xiao, Yong, Wang, Cheng-Xiang, Zhang, Zaichen, Ci, Song, Mao, Guoqiang, Li, Changle, Shao, Ziyu, Zhou, Yong, Liang, Junrui, Li, Kai, Wu, Liantao, Sun, Fanglei, Wang, Kunlun, Liu, Zening, Yang, Kun, Wang, Jun, Gao, Teng, and Shu, Hongfeng

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions., Comment: The current version has partial Insufficient completion, so we would like to withdraw it. We hope you agree, thank you

Published
2022

29. Attention Based Neural Networks for Wireless Channel Estimation

Author

Luan, Dianxin and Thompson, John

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence
Abstract

In this paper, we deploy the self-attention mechanism to achieve improved channel estimation for orthogonal frequency-division multiplexing waveforms in the downlink. Specifically, we propose a new hybrid encoder-decoder structure (called HA02) for the first time which exploits the attention mechanism to focus on the most important input information. In particular, we implement a transformer encoder block as the encoder to achieve the sparsity in the input features and a residual neural network as the decoder respectively, inspired by the success of the attention mechanism. Using 3GPP channel models, our simulations show superior estimation performance compared with other candidate neural network methods for channel estimation.

Published
2022

30. Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Author

Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J, Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y, Chang, Ken, Balana, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S, Lombardo, Joseph, Palmer, Joshua D, Flanders, Adam E, Dicker, Adam P, Sair, Haris I, Jones, Craig K, Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y, Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A, Mitchell, J Ross, Farinhas, Joaquim, Maldjian, Joseph A, Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C, Reddy, Divya, Holcomb, James, Wagner, Benjamin C, Ellingson, Benjamin M, Cloughesy, Timothy F, Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B, Teixeira, Bernardo C A, Sprenger, Flávia, Menotti, David, Lucio, Diego R, LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W, McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E, Vadmal, Vachan, Waite, Kristin, Colen, Rivka R, Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V M, Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I, Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M, Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R, Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten MJ, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W, Dubbink, Hendrikus J, Vincent, Arnaud JPE, Bent, Martin J van den, French, Pim J, Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P, Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B, Mistry, Akshitkumar, Thompson, Reid C, Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C, Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G H, Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M, Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F, Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M, Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A, Ogbole, Godwin, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu'aibu, Mustapha, Dorcas, Adeleye, Soneye, Mayowa, Dako, Farouk, Simpson, Amber L, Hamghalam, Mohammad, Peoples, Jacob J, Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y, Boss, Michael A, Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S, Martin, Jason, and Bakas, Spyridon

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing., Comment: federated learning, deep learning, convolutional neural network, segmentation, brain tumor, glioma, glioblastoma, FeTS, BraTS

Published
2022
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33. Low Complexity Channel estimation with Neural Network Solutions

Author

Luan, Dianxin and Thompson, John

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence
Abstract

Research on machine learning for channel estimation, especially neural network solutions for wireless communications, is attracting significant current interest. This is because conventional methods cannot meet the present demands of the high speed communication. In the paper, we deploy a general residual convolutional neural network to achieve channel estimation for the orthogonal frequency-division multiplexing (OFDM) signals in a downlink scenario. Our method also deploys a simple interpolation layer to replace the transposed convolutional layer used in other networks to reduce the computation cost. The proposed method is more easily adapted to different pilot patterns and packet sizes. Compared with other deep learning methods for channel estimation, our results for 3GPP channel models suggest improved mean squared error performance for our approach., Comment: Accepted by WSA 21

Published
2022

34. TENAYA and LUCERNE: Two-Year Results from the Phase 3 Neovascular Age-Related Macular Degeneration Trials of Faricimab with Treat-and-Extend Dosing in Year 2

Author

Abbey, Ashkan, Abdulaeva, Elmira, Abraham, Prema, Adan Civera, Alfredo, Agostini, Hansjurgen, Alezzandrini, Arturo, Alfaro, Virgil, Almony, Arghavan, Altay, Lebriz, Amini, Payam, Antoszyk, Andrew, Aradi, Etelka, Arias, Luis, Arnold, Jennifer, Asaria, Riaz, Astakhov, Sergei, Astakhov, Yury, Awh, Carl C., Balaratnasingam, Chandra, Banerjee, Sanjiv, Baumal, Caroline, Becker, Matthias, Belfort, Rubens, Jr., Bratko, Galina, Bridges, William Z., Jr., Brown, Jamin, Brown, David M., Budzinskaya, Maria, Buffet, Sylvia, Burgess, Stuart, Byon, Iksoo, Cagini, Carlo, Calzada, Jorge, Cameron, Stone, Campochiaro, Peter, Carlson, John, Carneiro, Angela, Chan, Clement, Chang, Emmanuel, Chang, Andrew, Chao, Daniel, Chaudhry, Nauman, Chee, Caroline, Cheek, Andrew, Chen, Shih-Jen, Chen, San-Ni, Cheung, Gemmy, Chexal, Saradha, Chittum, Mark, Chow, David, Cole, Abosede, Connolly, Brian, Cornut, Pierre Loic, Couvillion, Stephen, Danzig, Carl, Daskalov, Vesselin, Dessouki, Amr, Devin, Francois, Dollin, Michael, Dolz, Rosa, Downey, Louise, Dreyer, Richard, Dugel, Pravin, Eichenbaum, David, Eldem, Bora, Engstrom, Robert, Escobar, Joan Josep, Eter, Nicole, Faber, David W., Falk, Naomi, Feiner, Leonard, Vega, Alvaro Fernandez, Ferrone, Philip, Figueroa, Marta, Fine, Howard, Fineman, Mitchell, Fox, Gregory M., Francais, Catherine, Franco, Pablo, Fraser-Bell, Samantha, Fung, Nicholas, Sola, Federico Furno, Gale, Richard, Garcia-Layana, Alfredo, Gasperini, Julie, Gawecki, Maciej, Ghanchi, Faruque, Gill, Manjot, Giunta, Michel, Glaser, David, Goldstein, Michaella, Ulla, Francisco Gomez, Gomi, Fumi, Gonzalez, Victor, Graff, Jordan, Gupta, Sunil, Guthoff, Rainer, Guymer, Robyn, Haas, Anton, Hampton, Robert, Hatz, Katja, Hayashi, Ken, Heier, Jeffrey, Herba, Ewa, Hershberger, Vrinda, Higgins, Patrick, Holekamp, Nancy, Honda, Shigeru, Howard, James, Hu, Allen, Huddleston, Stephen, Iida, Tomohiro, Imaizumi, Hiroko, Ito, Yasuo, Ito, Yasuki, Itty, Sujit, Javey, Golnaz, Javid, Cameron, Kaga, Tatsushi, Kaluzny, Jakub, Kang, Se Woong, Kapoor, Kapil, Karabas, Levent, Kawasaki, Tsutomu, Kelty, Patrick, Kerenyi, Agnes, Khanani, Arshad, Khoramnia, Ramin, Khurana, Rahul, Kimura, Kazuhiro, Klein-Mascia, Kendra, Kobayashi, Namie, Kodjikian, Laurent, Koizumi, Hideki, Kokame, Gregg, Kulikov, Alexey, Kwong, Henry, Kwun, Robert, Lai, Timothy, Lai, Chi-Chun, Lalonde, Laurent, Lanzetta, Paolo, Larsen, Michael, Lavina, Adrian, Lee, Won Ki, Lee, ji Eun, Lee, Seong, Levy, Jaime, Lindsell, Lucas, Liu, Mimi, London, Nikolas, Lotery, Andrew, Lozano Rechy, David, Luckie, Alan, Maberley, David, Maeno, Takatoshi, Mahmood, Sajjad, Makkouk, Fuad, Marcus, Dennis, Margherio, Alan, Masse, Helene, Matsubara, Hisashi, Maturi, Raj, Mehta, Sonia, Menon, Geeta, Mentes, Jale, Michels, Mark, Mitamura, Yoshinori, Mitchell, Paul, Mohamed, Quresh, Mones, Jordi, Lobo, Rodrigo Montemayor, Montero, Javier, Moore, Jeffrey, Mori, Ryusaburo, Morori-Katz, Haia, Mukherjee, Raj, Murata, Toshinori, Muzyka-Wozniak, Maria, Nardi, Marco, Narendran, Niro, Nicolo, Massimo, Nielsen, Jared, Nishimura, Tetsuya, Noda, Kousuke, Nowinska, Anna, Oh, Hideyasu, Ohr, Matthew, Okada, Annabelle, Oleksy, Piotr, Ono, Shinji, Ozdek, Sengul, Ozturk, Banu, Pablo, Luis, Park, Kyu Hyung, Parke, D. Wilk, Parravano, Maria Cristina, Patel, Praveen, Patel, Apurva, Patel, Sunil, Patel, Sugat, Pauleikhoff, Daniel, Pearce, Ian, Pearlman, Joel, Petkova, Iva, Pieramici, Dante, Pozdeyeva, Nadezhda, Qureshi, Jawad, Raczynska, Dorota, Ramirez Estudillo, Juan, Rathod, Rajiv, Razavi, Hessam, Regillo, Carl, Reilly, Gayatri, Ricci, Federico, Rich, Ryan, Romanowska-Dixon, Bożena, Rosenblatt, Irit, Ruiz Moreno, Jose Maria, Sacu, Stefan, Saedon, Habiba, Saeed, Usman, Sagong, Min, Sakamoto, Taiji, Sandhu, Sukhpal, Sararols, Laura, Saravia, Mario, Schadlu, Ramin, Schlottmann, Patricio, Sekiryu, Tetsuju, Seres, András, Sermet, Figen, Shah, Sumit, Shah, Rohan, Shah, Ankur, Sheidow, Thomas, Sheth, Veeral, Shiragami, Chieko, Sikorski, Bartosz, Silva, Rufino, Singerman, Lawrence, Sisk, Robert, Sørensen, Torben L., Souied, Eric, Spinak, David-J., Staurenghi, Giovanni, Steinmetz, Robert, Stoller, Glenn, Stoltz, Robert, Suan, Eric, Suner, Ivan, Yzer, Suzanne, Tadayoni, Ramin, Takahashi, Kanji, Takayama, Kei, Taleb, Alexandre, Talks, James, Terasaki, Hiroko, Thompson, John, Toth-Molnar, Edit, Tran, Khoi, Tuli, Raman, Uchiyama, Eduardo, Vajas, Attila, Lith-Verhoeven, Janneke Van, Varsanyi, Balazs, Viola, Francesco, Virgili, Gianni, Vogt, Gábor, Völker, Michael, Warrow, David, Weber, Pamela, Wells, John A., Wickremasinghe, Sanjeewa, Wieland, Mark, Williams, Geoff, Williams, Thomas, Wong, David, Wong, King, Wong, James, Wong, Ian, Wong, Robert, Wowra, Bogumil, Wykoff, Charles C., Yamashita, Ayana, Yasuda, Kanako, Yilmaz, Gursel, Yiu, Glenn, Yoneda, Ai, Yoon, Young Hee, Yoreh, Barak, Yu, Hyeong Gon, Yu, Seung Young, Yurieva, Tatiana, Zambrano, Alberto, Zatorska, Barbara, Zeolite, Carlos, Khanani, Arshad M., Kotecha, Aachal, Chen, Youxin, Heier, Jeffrey S., Holz, Frank G., Ives, Jane A., Lim, Jennifer I., Lin, Hugh, Michels, Stephan, Quezada Ruiz, Carlos, Schmidt-Erfurth, Ursula, Silverman, David, Singh, Rishi, Swaminathan, Balakumar, and Willis, Jeffrey R.

Published
2024
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38. ASO Visual Abstract: Role of Concurrent Ultrasound Surveillance of Sentinel Node-Positive Node Fields in Melanoma Patients Having Routine Cross-Sectional Imaging

Author

Gjorup, Caroline A., Woodford, Rachel, Li, Isabel, Carlino, Matteo S., Ch’ng, Sydney, Chung, David, Hsiao, Edward, Lo, Serigne N., London, Kevin, Long, Georgina V., Menzies, Alexander M., Nieweg, Omgo E., Pennington, Thomas E., Rtshiladze, Michael A., Saw, Robyn P. M., Scolyer, Richard A., Shannon, Kerwin F., Spillane, Andrew J., Stretch, Jonathan R., Thompson, John F., Varey, Alexander H. R., and van Akkooi, Alexander C. J.

Published
2024
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40. Faricimab Treat-and-Extend for Diabetic Macular Edema: Two-Year Results from the Randomized Phase 3 YOSEMITE and RHINE Trials

Author

Aaberg, Thomas, Jr., Abbey, Ashkan, Abdulaeva, Elmira, Abengoechea, Santiago, Abraham, Prema, Ach, Thomas, Adams, Serrhel, Adan Civera, Alfredo, Adrean, Sean, Agostini, Hansjurgen, Alam, Suhail, Alezzandrini, Arturo, Alfaro, Virgil, Aliseda, Daniel, Almony, Arghavan, Amat, Pedro, Amini, Payam, Antoszyk, Andrew, Arias, Luis, Asaria, Riaz, Avila, Marcos, Awh, Carl C., Bafalluy, Joaquin, Baker, Carl, Bandello, Francesco, Barakat, Mark, Barraza, Karen, Bator, Gyorgy, Baumal, Caroline, Belfort, Rubens, Jr., Bergstrom, Chris, Bertolucci, George, Bochow, Thomas, Bolz, Matthias, Borcz, Emilia, Bordon, Arnaldo, Boyer, David, Bratko, Galina, Brent, Michael, Brown, Jamin, Brown, David M., Budzinskaya, Maria, Buffet, Sylvia, Burgess, Stuart, Burton, Ben, Busquets, Miguel, Cabrera, Francisco, Cagini, Carlo, Calzada, Jorge, Campochiaro, Peter, Carlson, John, Castellarin, Alessandro, Cava, Carlos, Chaikitmongkol, Voraporn, Chan, Clement, Chang, Emmanuel, Chang, Jonathan, Chang, Andrew, Charles, Steve, Chaudhry, Nauman, Chee, Caroline, Chen, Judy, Chen, Fred, Chen, Shih-Jen, Cheong-Leen, Richard, Chiang, Allen, Chittum, Mark, Chow, David, Connolly, Brian, Cornut, Pierre Loic, Csaky, Karl, Danzig, Carl, Das, Arup, Daskalov, Vesselin, Desco, Carmen, Dessouki, Amr, Dickinson, John, Do, Brian, Dollin, Michael, Dugel, Pravin, Dusova, Jaroslava, Eichenbaum, David, Eldem, Bora, Engstrom, Robert, Ernest, Jan, Escobar, Joan Josep, Esposti, Simona, Eter, Nicole, Falk, Naomi, Farkas, Andrej, Feiner, Leonard, Feltgen, Nicolas, Fernandez, Carlos, Fernandez Vega, Alvaro, Ferrone, Philip, Figueira, Joao, Figueroa, Marta, Findl, Oliver, Fine, Howard, Fortun, Jorge, Fox, Gregory M., Foxman, Scott, Framme, Carsten, Fraser-Bell, Samantha, Fu, Arthur, Fukutomi, Akira, Fung, Nicholas, Furno Sola, Federico, Gallego-Pinazo, Roberto, Garcia, Renata, Garcia-Layana, Alfredo, Gawecki, Maciej, George, Sheen, Ghanchi, Faruque, Ghorayeb, Ghassan, Goldberg, Roger, Goldstein, Michaella, Gomes, Nuno, Ulla, Francisco Gomez, Gonzalez, Victor, Greven, Craig, Gupta, Sunil, Guzman, Miguel, Harris, Martin, Hatz, Katja, Hau, Vivienne, Hau, Vincent, Hayashi, Ken, Heier, Jeffrey, Herba, Ewa, Hershberger, Vrinda, Higgins, Patrick, Hirakata, Akito, Ho, Allen, Holekamp, Nancy, Honda, Shigeru, Hsu, Jason, Hu, Allen, Hurcikova, Maria, Ikeda, Yasuhiro, Isernhagen, Ricky, Ito, Yasuki, Jackson, Tim, Jacoby, Rachael, Jafree, Afsar, Javey, Golnaz, Javid, Cameron, Jhaveri, Chirag, Johnson, Mark, Kacerík, Marek, Kaluzny, Jakub, Kampik, Daniel, Kang, Se Woong, Kapoor, Kapil, Karabas, Levent, Kawasaki, Tsutomu, Kerenyi, Agnes, Khanani, Arshad, Khurana, Rahul, Kim, Brian, Kimura, Kazuhiro, Kishino, Genichiro, Kitano, Shigehiko, Klein-Mascia, Kendra, Kokame, Gregg, Korobelnik, Jean Francois, Kulikov, Alexey, Kuriyan, Ajay, Kwong, Henry, Kwun, Robert, Lai, Timothy, Lai, Chi-Chun, Laird, Philip, Lalonde, Laurent, Lanzetta, Paolo, Larsen, Michael, Laugesen, Caroline, Lavinsky, Daniel, Lebreton, Olivier, Lee, Seong, Levy, Jaime, Lipkova, Blandina, Liu, Mimi, Liu, Judy, Lohmann, Chris P., London, Nikolas, Lorenz, Katrin, Lotery, Andrew, Lozano Rechy, David, Lujan, Silvio, Ma, Patrick, Maeno, Takatoshi, Mahmood, Sajjad, Makkouk, Fuad, Malik, Khurram, Marcus, Dennis, Margherio, Alan, Mastropasqua, Leonardo, Maturi, Raj, McCabe, Frank, McKibbin, Martin, Mehta, Hemal, Menon, Geeta, Mentes, Jale, Michalska-Malecka, Katarzyna, Misheva, Aneta, Mitamura, Yoshinori, Mitchell, Paul, Modi, Yasha, Mohamed, Quresh, Montero, Javier, Moore, Jeffrey, Morales Canton, Virgilio, Morori-Katz, Haia, Morugova, Tatiana, Murakami, Tomoaki, Muzyka-Wozniak, Maria, Nardi, Marco, Nemcansky, Jan, Nester-Ostrowska, Kamila, Neto, Julio, Newell, Charles, Nicolo, Massimo, Nielsen, Jared, Noda, Kousuke, Obana, Akira, Ogata, Nahoko, Oh, Hideyasu, Oh, Kean, Ohr, Matthew, Oleksy, Piotr, Oliver, Scott, Olivier, Sebastien, Osher, James, Ozcalışkan, Sehnaz, Ozturk, Banu, Papp, Andras, Park, Kyu Hyung, Parke, D. Wilkin, Parravano, Maria Cristina, Patel, Sugat, Patel, Sunil, Pearce, Ian, Pearlman, Joel, Penha, Fernando, Perente, Irfan, Perkins, Stephen, Pertile, Grazia, Petkova, Iva, Peto, Tunde, Pieramici, Dante, Pollreisz, Andreas, Pongsachareonnont, Pear, Pozdeyeva, Nadezhda, Priglinger, Siegfried, Qureshi, Jawad, Raczynska, Dorota, Rajagopalan, Rajesh, Ramirez Estudillo, Juan, Raskauskas, Paul, Rathod, Rajiv, Razavi, Hessam, Regillo, Carl, Ricci, Federico, Rofagha, Soraya, Romanczak, Dominika, Romanowska-Dixon, Bożena, Rosberger, Daniel, Rosenblatt, Irit, Rosenblatt, Brett, Ross, Adam, Ruamviboonsuk, Paisan, Ruiz Moreno, Jose Maria, Salomão, Gustavo, Sandhu, Sukhpal, Sandner, Dirk, Sararols, Laura, Sawada, Osamu, Schadlu, Ramin, Schlottmann, Patricio, Schuart, Claudia, Seitz, Berthold, Seres, András, Sermet, Figen, Shah, Sandeep, Shah, Ankur, Shah, Rohan, Sharma, Sumit, Sheidow, Thomas, Sheth, Veeral, Shimouchi, Akito, Shimura, Masahiko, Sikorski, Bartosz, Silva, Rufino, Singer, Michael, Singerman, Lawrence, Singh, Rishi, Souied, Eric, Spinak, David J., Spital, Georg, Steinle, Nathan, Stern, Jeffrey, Stoller, Glenn, Stoltz, Robert, Stone, Cameron, Stone, Amy, Suan, Eric, Sugimoto, Masahiko, Sugita, Iichiro, Sun, Jennifer, Sun, Xiaodong, Suner, Ivan, Szalczer, Lajos, Szecsko, Timea, Tabassian, Ali, Tadayoni, Ramin, Takagi, Hitoshi, Takayama, Kei, Taleb, Alexandre, Talks, James, Tan, Gavin, Tanabe, Teruyo, Taylor, Stanford, Thach, Allen, Thompson, John, Tlucek, Paul, Torti, Robert, Tosheva Guneva, Daniela, Toth-Molnar, Edit, Uchiyama, Eduardo, Vajas, Attila, Varma, Deepali, Varsanyi, Balazs, Vassileva, Petja, Vaz-Pereira, Sara, Veith, Miroslav, Vela, Jose Ignacio, Viola, Francesco, Virgili, Gianni, Vogt, Gábor, Vorum, Henrik, Weber, Pamela, Wecke, Thoalf, Wee, Raymond, Weger, Martin, Weishaar, Paul, Wells, John A., Wickremasinghe, Sanjeewa, Williams, Thomas Reginald, Williams, Thomas, Williams, Geoff, Wolf, Armin, Wolfe, Jeremy, Wong, James, Wong, David, Wong, Ian, Wong, Robert, Wowra, Bogumil, Wykoff, Charles C., Wylęgała, Edward, Yang, Chang-Hao, Yasukawa, Tsutomu, Yates, Paul, Yilmaz, Gursel, Yiu, Glenn, Yoon, Young Hee, Yoreh, Barak, Yoshida, Shigeo, Yu, Hyeong Gon, Yu, Seung Young, Yurieva, Tatiana, Zacharias, Leandro, Zaczek Zakrzewska, Karolina, Zambrano, Alberto, Zatorska, Barbara, Zeolite, Carlos, Zheutlin, Jeffrey, Wong, Tien Y., Haskova, Zdenka, Asik, Kemal, Baumal, Caroline R., Csaky, Karl G., Ives, Jane A., Jaffe, Glenn J., Korobelnik, Jean-François, Lin, Hugh, Murata, Toshinori, Schlottmann, Patricio G., Seres, András I., Silverman, David, and Tang, Yannan

Published
2024
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42. Development and validation of a novel model to predict recurrence-free survival and melanoma-specific survival after sentinel lymph node biopsy in patients with melanoma: an international, retrospective, multicentre analysis

Author

Stassen, Robert C, Maas, Carolien C H M, van der Veldt, Astrid A M, Lo, Serigne N, Saw, Robyn P M, Varey, Alexander H R, Scolyer, Richard A, Long, Georgina V, Thompson, John F, Rutkowski, Piotr, Keilholz, Ulrich, van Akkooi, Alexander C J, Verhoef, Cornelis, van Klaveren, David, and Grünhagen, Dirk J

Published
2024
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45. Uncovering the complex relationship between balding, testosterone and skin cancers in men

Author

Ong, Jue-Sheng, Seviiri, Mathias, Dusingize, Jean Claude, Wu, Yeda, Han, Xikun, Shi, Jianxin, Olsen, Catherine M., Neale, Rachel E., Thompson, John F., Saw, Robyn P. M., Shannon, Kerwin F., Mann, Graham J., Martin, Nicholas G., Medland, Sarah E., Gordon, Scott D., Scolyer, Richard A., Long, Georgina V., Iles, Mark M., Landi, Maria Teresa, Whiteman, David C., MacGregor, Stuart, and Law, Matthew H.

Published
2023
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