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1. Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments.

Author

Farris, Alton, Alexander, Mariam, Balis, Ulysses, Barisoni, Laura, Boor, Peter, Bülow, Roman, Cornell, Lynn, Demetris, Anthony, Farkash, Evan, Hermsen, Meyke, Hogan, Julien, Kain, Renate, Kers, Jesper, Kong, Jun, Levenson, Richard, Loupy, Alexandre, Naesens, Maarten, Sarder, Pinaki, Tomaszewski, John, van der Laak, Jeroen, van Midden, Dominique, Yagi, Yukako, and Solez, Kim

Subjects
Banff, artificial intelligence, digital pathology, image analysis, machine learning, Humans, Artificial Intelligence, Algorithms, Kidney Transplantation, Kidney
Abstract

The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.

Published
2023

4. Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

Author

Bejnordi, Babak Ehteshami, Zuidhof, Guido, Balkenhol, Maschenka, Hermsen, Meyke, Bult, Peter, van Ginneken, Bram, Karssemeijer, Nico, Litjens, Geert, and van der Laak, Jeroen

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area. In this paper, we present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We first train a CNN using high pixel resolution patches to capture cellular level information. The feature responses generated by this model are then fed as input to a second CNN, stacked on top of the first. Training of this stacked architecture with large input patches enables learning of fine-grained (cellular) details and global interdependence of tissue structures. Our system is trained and evaluated on a dataset containing 221 WSIs of H&E stained breast tissue specimens. The system achieves an AUC of 0.962 for the binary classification of non-malignant and malignant slides and obtains a three class accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC, demonstrating its potentials for routine diagnostics.

Published
2017

6. Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments

Author

Farris, Alton B., primary, Alexander, Mariam P., additional, Balis, Ulysses G. J., additional, Barisoni, Laura, additional, Boor, Peter, additional, Bülow, Roman D., additional, Cornell, Lynn D., additional, Demetris, Anthony J., additional, Farkash, Evan, additional, Hermsen, Meyke, additional, Hogan, Julien, additional, Kain, Renate, additional, Kers, Jesper, additional, Kong, Jun, additional, Levenson, Richard M., additional, Loupy, Alexandre, additional, Naesens, Maarten, additional, Sarder, Pinaki, additional, Tomaszewski, John E., additional, van der Laak, Jeroen, additional, van Midden, Dominique, additional, Yagi, Yukako, additional, and Solez, Kim, additional

Published
2023
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7. Whole Mastectomy Volume Reconstruction from 2D Radiographs and Its Mapping to Histology

Author

Mertzanidou, Thomy, Hipwell, John H., Reis, Sara, Bejnordi, Babak Ehteshami, Hermsen, Meyke, Dalmis, Mehmet, Vreemann, Suzan, Platel, Bram, van der Laak, Jeroen, Karssemeijer, Nico, Mann, Ritse, Bult, Peter, Hawkes, David J., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tingberg, Anders, editor, Lång, Kristina, editor, and Timberg, Pontus, editor

Published
2016
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9. Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments

Author

Farris, Alton B., Alexander, Mariam P., Balis, Ulysses G. J., Barisoni, Laura, Boor, Peter, Bulow, Roman D., Cornell, Lynn D., Demetris, Anthony J., Farkash, Evan, Hermsen, Meyke, Hogan, Julien, Kain, Renate, Kers, Jesper, Kong, Jun, Levenson, Richard M., Loupy, Alexandre, Naesens, Maarten, Sarder, Pinaki, Tomaszewski, John E., van der Laak, Jeroen, van Midden, Dominique, Yagi, Yukako, Solez, Kim, Farris, Alton B., Alexander, Mariam P., Balis, Ulysses G. J., Barisoni, Laura, Boor, Peter, Bulow, Roman D., Cornell, Lynn D., Demetris, Anthony J., Farkash, Evan, Hermsen, Meyke, Hogan, Julien, Kain, Renate, Kers, Jesper, Kong, Jun, Levenson, Richard M., Loupy, Alexandre, Naesens, Maarten, Sarder, Pinaki, Tomaszewski, John E., van der Laak, Jeroen, van Midden, Dominique, Yagi, Yukako, and Solez, Kim

Abstract

The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community., Funding Agencies|PB is supported by the German Research Foundation (DFG, Project IDs 322900939 amp; 445703531), European Research Council (ERC Consolidator Grant No 101001791), the Federal Ministries of Education and Research [BMBF, STOP-FSGS-01GM2202C; Economic Affairs a [322900939, 445703531]; German Research Foundation (DFG) [101001791]; European Research Council [STOP-FSGS-01GM2202C]; Federal Ministries of Education and Research [BMBF] [01MK2002A, ZMVI1-2520DAT111]; Economic Affairs and Climate Action [01VSF21048]; Innovation Fund of the Federal Joint Committee [U01CA242936]; National Institutes of Health [945358]; Innovative Medicines Initiative 2 Joint Undertaking; European Union; EFPIA [17OKG23, 21OK+012]; Dutch Kidney Foundation; Human(e) AI research priority area of the University of Amsterdam [S003422N, G087620N]; Research Foundation Flanders (FWO) [C32/17/049, 1844019N]; KU Leuven C3 internal grant

Published
2023
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11. Human scattered tubular cells represent a heterogeneous population of glycolytic dedifferentiated proximal tubule cells

Author

Eymael, Jennifer, primary, van den Broek, Martijn, additional, Miesen, Laura, additional, Monge, Valerie Villacorta, additional, van den Berge, Bartholomeus T, additional, Mooren, Fieke, additional, Velez, Vicky Luna, additional, Dijkstra, Jelmer, additional, Hermsen, Meyke, additional, Bándi, Péter, additional, Vermeulen, Michiel, additional, de Wildt, Saskia, additional, Willemsen, Brigith, additional, Florquin, Sandrine, additional, Wetzels, Roy, additional, Steenbergen, Eric, additional, Kramann, Rafael, additional, Moeller, Marcus, additional, Schreuder, Michiel F, additional, Wetzels, Jack FM, additional, van der Vlag, Johan, additional, Jansen, Jitske, additional, and Smeets, Bart, additional

Published
2022
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13. Adjunctive use of p16 immunohistochemistry for optimizing management of CIN lesions in a high‐risk human papillomavirus‐positive population

Author

Ebisch, Renée M. F., primary, Rijstenberg, L. Lucia, additional, Soltani, Gilda Ghazi, additional, van der Horst, Judith, additional, Vedder, Judith E. M., additional, Hermsen, Meyke, additional, Bosgraaf, Remko P., additional, Massuger, Leon F. A. G., additional, Meijer, Chris J. L. M., additional, Heideman, Daniëlle A. M., additional, van Kemenade, Folkert J., additional, Melchers, Willem J. G., additional, Bekkers, Ruud L. M., additional, Siebers, Albert G., additional, and Bulten, Johan, additional

Published
2022
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14. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

Author

Ehteshami Bejnordi, Babak, Veta, Mitko, Johannes van Diest, Paul, van Ginneken, Bram, Karssemeijer, Nico, Litjens, Geert, van der Laak, Jeroen A. W. M., Hermsen, Meyke, Manson, Quirine F, Balkenhol, Maschenka, Geessink, Oscar, Stathonikos, Nikolaos, van Dijk, Marcory CRF, Bult, Peter, Beca, Francisco, Beck, Andrew H, Wang, Dayong, Khosla, Aditya, Gargeya, Rishab, Irshad, Humayun, Zhong, Aoxiao, Dou, Qi, Li, Quanzheng, Chen, Hao, Lin, Huang-Jing, Heng, Pheng-Ann, Haß, Christian, Bruni, Elia, Wong, Quincy, Halici, Ugur, Öner, Mustafa Ümit, Cetin-Atalay, Rengul, Berseth, Matt, Khvatkov, Vitali, Vylegzhanin, Alexei, Kraus, Oren, Shaban, Muhammad, Rajpoot, Nasir, Awan, Ruqayya, Sirinukunwattana, Korsuk, Qaiser, Talha, Tsang, Yee-Wah, Tellez, David, Annuscheit, Jonas, Hufnagl, Peter, Valkonen, Mira, Kartasalo, Kimmo, Latonen, Leena, Ruusuvuori, Pekka, Liimatainen, Kaisa, Albarqouni, Shadi, Mungal, Bharti, George, Ami, Demirci, Stefanie, Navab, Nassir, Watanabe, Seiryo, Seno, Shigeto, Takenaka, Yoichi, Matsuda, Hideo, Ahmady Phoulady, Hady, Kovalev, Vassili, Kalinovsky, Alexander, Liauchuk, Vitali, Bueno, Gloria, Fernandez-Carrobles, M. Milagro, Serrano, Ismael, Deniz, Oscar, Racoceanu, Daniel, and Venâncio, Rui

Published
2017
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16. Adjunctive use of p16 immunohistochemistry for optimizing management of CIN lesions in a high-risk human papillomavirus-positive population

Author

Ebisch, Renee M. F., Rijstenberg, L. Lucia, Soltani, Gilda Ghazi, van der Horst, Judith, Vedder, Judith E. M., Hermsen, Meyke, Bosgraaf, Remko P., Massuger, Leon F. A. G., Meijer, Chris J. L. M., Heideman, Danielle A. M., van Kemenade, Folkert J., Melchers, Willem J. G., Bekkers, Ruud L. M., Siebers, Albert G., Bulten, Johan, Ebisch, Renee M. F., Rijstenberg, L. Lucia, Soltani, Gilda Ghazi, van der Horst, Judith, Vedder, Judith E. M., Hermsen, Meyke, Bosgraaf, Remko P., Massuger, Leon F. A. G., Meijer, Chris J. L. M., Heideman, Danielle A. M., van Kemenade, Folkert J., Melchers, Willem J. G., Bekkers, Ruud L. M., Siebers, Albert G., and Bulten, Johan

Abstract

Introduction Immunostaining with p16(INK4a) (p16), a tumor-suppressor surrogate protein biomarker for high-risk human papillomavirus (hrHPV) oncogenic activity, may complement standard hematoxylin and eosin (H&E) histology review, and provide more objective criteria to support the cervical intraepithelial neoplasia (CIN) diagnosis. With this study we assessed the impact of p16 immunohistochemistry on CIN grading in an hrHPV-based screening setting. Material and methods In this post-hoc analysis, 326 histology follow-up samples from a group of hrHPV-positive women were stained with p16 immunohistochemistry. All H&E samples were centrally revised. The pathologists reported their level of confidence in classifying the CIN lesion. Results Combining H&E and p16 staining resulted in a change of diagnosis in 27.3% (n = 89) of cases compared with the revised H&E samples, with a decrease of 34.5% (n = 18) in CIN1 and 22.7% (n = 15) in CIN2 classifications, and an increase of 18.3% (n = 19) in no CIN and 20.7% (n = 19) in CIN3 diagnoses. The level of confidence in CIN grading by the pathologist increased with adjunctive use of p16 immunohistochemistry to standard H&E. Conclusions This study shows that adjunctive use of p16 immunohistochemistry to H&E morphology reduces the number of CIN1 and CIN2 classifications with a proportional increase in no CIN and CIN3 diagnoses, compared with standard H&E-based CIN diagnosis alone. The pathologists felt more confident in classifying the material with H&E and p16 immunohistochemistry than by using H&E alone, particularly during assessment of small biopsies. Adjunctive use of p16 immunohistochemistry to standard H&E assessment of CIN would be valuable for the diagnostic accuracy, thereby optimizing CIN management and possibly decreasing overtreatment.

Published
2022

17. Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies

Author

Pathologie Opleiding, Pathologie Pathologen staf, Circulatory Health, Hermsen, Meyke, Ciompi, Francesco, Adefidipe, Adeyemi, Denic, Aleksandar, Dendooven, Amélie, Smith, Byron H, van Midden, Dominique, Bräsen, Jan Hinrich, Kers, Jesper, Stegall, Mark D, Bándi, Péter, Nguyen, Tri, Swiderska-Chadaj, Zaneta, Smeets, Bart, Hilbrands, Luuk B, van der Laak, Jeroen A W M, Pathologie Opleiding, Pathologie Pathologen staf, Circulatory Health, Hermsen, Meyke, Ciompi, Francesco, Adefidipe, Adeyemi, Denic, Aleksandar, Dendooven, Amélie, Smith, Byron H, van Midden, Dominique, Bräsen, Jan Hinrich, Kers, Jesper, Stegall, Mark D, Bándi, Péter, Nguyen, Tri, Swiderska-Chadaj, Zaneta, Smeets, Bart, Hilbrands, Luuk B, and van der Laak, Jeroen A W M

Published
2022

19. Whole Mastectomy Volume Reconstruction from 2D Radiographs and Its Mapping to Histology

Author

Mertzanidou, Thomy, primary, Hipwell, John H., additional, Reis, Sara, additional, Bejnordi, Babak Ehteshami, additional, Hermsen, Meyke, additional, Dalmis, Mehmet, additional, Vreemann, Suzan, additional, Platel, Bram, additional, van der Laak, Jeroen, additional, Karssemeijer, Nico, additional, Mann, Ritse, additional, Bult, Peter, additional, and Hawkes, David J., additional

Published
2016
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20. Human scattered tubular cells represent a heterogeneous population of glycolytic dedifferentiated proximal tubule cells.

Author

Eymael, Jennifer, van den Broek, Martijn, Miesen, Laura, Monge, Valerie Villacorta, van den Berge, Bartholomeus T, Mooren, Fieke, Velez, Vicky Luna, Dijkstra, Jelmer, Hermsen, Meyke, Bándi, Péter, Vermeulen, Michiel, de Wildt, Saskia, Willemsen, Brigith, Florquin, Sandrine, Wetzels, Roy, Steenbergen, Eric, Kramann, Rafael, Moeller, Marcus, Schreuder, Michiel F, and Wetzels, Jack FM

Subjects
KREBS cycle, ACUTE kidney failure, TUMOR necrosis factors, CELLULAR aging, RENAL tubular transport disorders, OXIDATIVE phosphorylation
Abstract

Scattered tubular cells (STCs) are a phenotypically distinct cell population in the proximal tubule that increase in number after acute kidney injury. We aimed to characterize the human STC population. Three‐dimensional human tissue analysis revealed that STCs are preferentially located within inner bends of the tubule and are barely present in young kidney tissue (<2 years), and their number increases with age. Increased STC numbers were associated with acute tubular injury (kidney injury molecule 1) and interstitial fibrosis (alpha smooth muscle actin). Isolated CD13+CD24−CD133− proximal tubule epithelial cells (PTECs) and CD13+CD24+ and CD13+CD133+ STCs were analyzed using RNA sequencing. Transcriptome analysis revealed an upregulation of nuclear factor κB, tumor necrosis factor alpha, and inflammatory pathways in STCs, whereas metabolism, especially the tricarboxylic acid cycle and oxidative phosphorylation, was downregulated, without showing signs of cellular senescence. Using immunostaining and a publicly available single‐cell sequencing database of human kidneys, we demonstrate that STCs represent a heterogeneous population in a transient state. In conclusion, STCs are dedifferentiated PTECs showing a metabolic shift toward glycolysis, which could facilitate cellular survival after kidney injury. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

Published
2023
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21. Automated scoring of total inflammation in renal allograft biopsies.

Author

Smith, Byron, Grande, Joseph, Ryan, Maggie, Smith, Maxwell, Denic, Aleksandar, Hermsen, Meyke, Park, Walter, Kremers, Walter, and Stegall, Mark

Subjects
RENAL biopsy, CONVOLUTIONAL neural networks, IMAGE analysis, INFLAMMATION, KIDNEY transplantation
Abstract

Background: Computer‐assisted scoring is gaining prominence in the evaluation of renal histology; however, much of the focus has been on identifying larger objects such as glomeruli. Total inflammation impacts graft outcome, and its quantification requires tools to identify objects at the cellular level or smaller. The goal of the current study was to use CD45 stained slides coupled with image analysis tools to quantify the amount of non‐glomerular inflammation within the cortex. Methods: Sixty renal transplant whole slide images were used for digital image analysis. Multiple thresholding methods using pixel intensity and object size were used to identify inflammation in the cortex. Additionally, convolutional neural networks were used to separate glomeruli from other objects in the cortex. This combined measure of inflammation was then correlated with rescored Banff total inflammation classification and outcomes. Results: Identification of glomeruli on biopsies had high fidelity (mean pixelwise dice coefficient of.858). Continuous total inflammation scores correlated well with Banff rescoring (maximum Pearson correlation.824). A separate set of thresholds resulted in a significant correlation with alloimmune graft loss. Conclusions: Automated scoring of inflammation showed a high correlation with Banff scoring. Digital image analysis provides a powerful tool for analysis of renal pathology, not only because it is reproducible and can be automated, but also because it provides much more granular data for studies. [ABSTRACT FROM AUTHOR]

Published
2023
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24. Stain-Transforming Cycle-Consistent Generative Adversarial Networks for Improved Segmentation of Renal Histopathology

Author

de Bel, Thomas, Hermsen, Meyke, Kers, Jesper, van der Laak, Jeroen, Litjens, Geert, Cardoso, M. Jorge, Feragen, Aasa, Glocker, Ben, Konukoglu, Ender, Oguz, Ipek, Unal, Gozde, Vercauteren, Tom, Pathology, AII - Inflammatory diseases, and APH - Global Health

Abstract

The performance of deep learning applications in digital histopathology can deteriorate significantly due to staining variations across centers. We employ cycle-consistent generative adversarial networks (cycleGANs) for unpaired image-to-image translation, facilitating between-center stain transformation. We find that modifications to the original cycleGAN architecture make it more suitable for stain transformation, creating artificially stained images of high quality. Specifically, changing the generator model to a smaller U-net-like architecture, adding an identity loss term, increasing the batch size and the learning all led to improved training stability and performance. Furthermore, we propose a method for dealing with tiling artifacts when applying the network on whole slide images (WSIs). We apply our stain transformation method on two datasets of PASstained (Periodic Acid-Schiff) renal tissue sections from different centers. We show that stain transformation is beneficial to the performance of cross-center segmentation, raising the Dice coefficient from 0.36 to 0.85 and from 0.45 to 0.73 on the two datasets.

Published
2019

25. Deep Learning-Based Histopathologic Assessment of Kidney Tissue

Author

Hermsen, Meyke, de Bel, Thomas, den Boer, Marjolijn, Steenbergen, Eric J., Kers, Jesper, Florquin, Sandrine, Roelofs, Joris J. T. H., Stegall, Mark D., Alexander, Mariam P., Smith, Byron H., Smeets, Bart, Hilbrands, Luuk B., van der Laak, Jeroen, Hermsen, Meyke, de Bel, Thomas, den Boer, Marjolijn, Steenbergen, Eric J., Kers, Jesper, Florquin, Sandrine, Roelofs, Joris J. T. H., Stegall, Mark D., Alexander, Mariam P., Smith, Byron H., Smeets, Bart, Hilbrands, Luuk B., and van der Laak, Jeroen

Abstract

Background The development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolutional neural network for multiclass segmentation of digitized kidney tissue sections stained with periodic acid-Schiff (PAS). Methods We trained the network using multiclass annotations from 40 whole-slide images of stained kidney transplant biopsies and applied it to four independent data sets. We assessed multiclass segmentation performance by calculating Dice coefficients for ten tissue classes on ten transplant biopsies from the Radboud University Medical Center in Nijmegen, The Netherlands, and on ten transplant biopsies from an external center for validation. We also fully segmented 15 nephrectomy samples and calculated the networks glomerular detection rates and compared network-based measures with visually scored histologic components (Banff classification) in 82 kidney transplant biopsies. Results The weighted mean Dice coefficients of all classes were 0.80 and 0.84 in ten kidney transplant biopsies from the Radboud center and the external center, respectively. The best segmented class was "glomeruli" in both data sets (Dice coefficients, 0.95 and 0.94, respectively), followed by "tubuli combined" and "interstitium." The network detected 92.7% of all glomeruli in nephrectomy samples, with 10.4% false positives. In whole transplant biopsies, the mean intraclass correlation coefficient for glomerular counting performed by pathologists versus the network was 0.94. We found significant correlations between visually scored histologic components and network-based measures. Conclusions This study presents the first convolutional neural network for multiclass segmentation of PAS-stained nephrectomy samples and transplant biopsies. Our network may have utility for quantitative studies involving kidney histopathology across centers and provide opportunities for deep learning applications in routine diagnostics., Funding Agencies|European UnionEuropean Union (EU) [9003035004]; Dutch Kidney Foundation (Nierstichting) [17OKG23]

Published
2019
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26. Developing image analysis pipelines of whole-slide images: Pre- and post-processing.

Author

Smith, Byron, Hermsen, Meyke, Lesser, Elizabeth, Ravichandar, Deepak, and Kremers, Walter

Abstract

Deep learning has pushed the scope of digital pathology beyond simple digitization and telemedicine. The incorporation of these algorithms in routine workflow is on the horizon and maybe a disruptive technology, reducing processing time, and increasing detection of anomalies. While the newest computational methods enjoy much of the press, incorporating deep learning into standard laboratory workflow requires many more steps than simply training and testing a model. Image analysis using deep learning methods often requires substantial pre- and post-processing order to improve interpretation and prediction. Similar to any data processing pipeline, images must be prepared for modeling and the resultant predictions need further processing for interpretation. Examples include artifact detection, color normalization, image subsampling or tiling, removal of errant predictions, etc. Once processed, predictions are complicated by image file size – typically several gigabytes when unpacked. This forces images to be tiled, meaning that a series of subsamples from the whole-slide image (WSI) are used in modeling. Herein, we review many of these methods as they pertain to the analysis of biopsy slides and discuss the multitude of unique issues that are part of the analysis of very large images. [ABSTRACT FROM AUTHOR]

Published
2021
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27. 1399 H&E-stained sentinel lymph node sections of breast cancer patients : The CAMELYON dataset

Author

Litjens, Geert, Bandi, Peter, Bejnordi, Babak Ehteshami, Geessink, Oscar, Balkenhol, Maschenka, Bult, Peter, Halilovic, Altuna, Hermsen, Meyke, van de Loo, Rob, Vogels, Rob, Manson, Quirine F., Stathonikos, Nikolas, Baidoshvili, Alexi, van Diest, Paul, Wauters, Carla, van Dijk, Marcory, and van der Laak, Jeroen

Subjects
Grand challenge, Breast cancer, Sentinel node, Whole-slide images, Health Informatics, Lymph node metastases, Computer Science Applications
Abstract

Background: The presence of lymph node metastases is one of the most important factors in breast cancer prognosis. The most common way to assess regional lymph node status is the sentinel lymph node procedure. The sentinel lymph node is the most likely lymph node to contain metastasized cancer cells and is excised, histopathologically processed, and examined by a pathologist. This tedious examination process is time-consuming and can lead to small metastases being missed. However, recent advances in whole-slide imaging and machine learning have opened an avenue for analysis of digitized lymph node sections with computer algorithms. For example, convolutional neural networks, a type of machine-learning algorithm, can be used to automatically detect cancer metastases in lymph nodes with high accuracy. To train machine-learning models, large, well-curated datasets are needed. Results: We released a dataset of 1,399 annotated whole-slide images (WSIs) of lymph nodes, both with and without metastases, in 3 terabytes of data in the context of the CAMELYON16 and CAMELYON17 Grand Challenges. Slides were collected from five medical centers to cover a broad range of image appearance and staining variations. Each WSI has a slide-level label indicating whether it contains no metastases, macro-metastases, micro-metastases, or isolated tumor cells. Furthermore, for 209 WSIs, detailed hand-drawn contours for all metastases are provided. Last, open-source software tools to visualize and interact with the data have been made available. Conclusions: A unique dataset of annotated, whole-slide digital histopathology images has been provided with high potential for re-use.

Published
2018

28. Deep Learning–Based Histopathologic Assessment of Kidney Tissue

Author

Hermsen, Meyke, primary, de Bel, Thomas, additional, den Boer, Marjolijn, additional, Steenbergen, Eric J., additional, Kers, Jesper, additional, Florquin, Sandrine, additional, Roelofs, Joris J. T. H., additional, Stegall, Mark D., additional, Alexander, Mariam P., additional, Smith, Byron H., additional, Smeets, Bart, additional, Hilbrands, Luuk B., additional, and van der Laak, Jeroen A. W. M., additional

Published
2019
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29. From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

Author

Bandi, Peter, primary, Geessink, Oscar, additional, Manson, Quirine, additional, Van Dijk, Marcory, additional, Balkenhol, Maschenka, additional, Hermsen, Meyke, additional, Ehteshami Bejnordi, Babak, additional, Lee, Byungjae, additional, Paeng, Kyunghyun, additional, Zhong, Aoxiao, additional, Li, Quanzheng, additional, Zanjani, Farhad Ghazvinian, additional, Zinger, Svitlana, additional, Fukuta, Keisuke, additional, Komura, Daisuke, additional, Ovtcharov, Vlado, additional, Cheng, Shenghua, additional, Zeng, Shaoqun, additional, Thagaard, Jeppe, additional, Dahl, Anders B., additional, Lin, Huangjing, additional, Chen, Hao, additional, Jacobsson, Ludwig, additional, Hedlund, Martin, additional, cetin, Melih, additional, Halici, Eren, additional, Jackson, Hunter, additional, Chen, Richard, additional, Both, Fabian, additional, Franke, Jorg, additional, Kusters-Vandevelde, Heidi, additional, Vreuls, Willem, additional, Bult, Peter, additional, van Ginneken, Bram, additional, van der Laak, Jeroen, additional, and Litjens, Geert, additional

Published
2019
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30. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Author

Bejnordi, Babak Ehteshami, Veta, Mitko, Van Diest, Paul Johannes, Van Ginneken, Bram, Karssemeijer, Nico, Litjens, Geert, van der Laak, Jeroen A W M, Hermsen, Meyke, Manson, Quirine F., Balkenhol, Maschenka, Geessink, Oscar, Stathonikos, Nikolaos, van Dijk, Marcory C R F, Bult, Peter, Beca, Francisco, Beck, Andrew H., Wang, Dayong, Khosla, Aditya, Gargeya, Rishab, Irshad, Humayun, Zhong, Aoxiao, Dou, Qi, Li, Quanzheng, Chen, Hao, Lin, Huang Jing, Heng, Pheng-Ann, Haß, Christian, Bruni, Elia, Wong, Quincy, Halici, Ugur, Öner, Mustafa Ümit, Cetin-Atalay, Rengul, Berseth, Matt, Khvatkov, Vitali, Vylegzhanin, Alexei, Kraus, Oren, Shaban, Muhammad, Rajpoot, Nasir M., Awan, Ruqayya, Sirinukunwattana, Korsuk, Qaiser, Talha, Tsang, Yee Wah, Tellez, David, Annuscheit, Jonas, Hufnagl, Peter, Valkonen, Mira, Kartasalo, Kimmo, Latonen, Leena, Ruusuvuori, Pekka, Liimatainen, Kaisa, and CAMELYON16 Consortium

Subjects
Machine Learning, Pathologists, Medicine(all), Breast Neoplasms/pathology, Pathology, Clinical, ROC Curve, Lymphatic Metastasis/diagnosis, Journal Article, Humans, Female, Comparative Study, Algorithms
Abstract

Importance: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting. Design, Setting, and Participants: Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures: Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures: The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results: The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P

Published
2017

31. 1399 H&E-stained sentinel lymph node sections of breast cancer patients: The CAMELYON dataset

Author

Pathologie, Cancer, Litjens, Geert, Bandi, Peter, Bejnordi, Babak Ehteshami, Geessink, Oscar, Balkenhol, Maschenka, Bult, Peter, Halilovic, Altuna, Hermsen, Meyke, van de Loo, Rob, Vogels, Rob, Manson, Quirine F., Stathonikos, Nikolas, Baidoshvili, Alexi, van Diest, Paul, Wauters, Carla, van Dijk, Marcory, van der Laak, Jeroen, Pathologie, Cancer, Litjens, Geert, Bandi, Peter, Bejnordi, Babak Ehteshami, Geessink, Oscar, Balkenhol, Maschenka, Bult, Peter, Halilovic, Altuna, Hermsen, Meyke, van de Loo, Rob, Vogels, Rob, Manson, Quirine F., Stathonikos, Nikolas, Baidoshvili, Alexi, van Diest, Paul, Wauters, Carla, van Dijk, Marcory, and van der Laak, Jeroen

Published
2018

32. From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge

Author

Bandi, Peter, Geessink, Oscar, Manson, Quirine, van Dijk, Marcory, Balkenhol, Maschenka, Hermsen, Meyke, Bejnordi, Babak Ehteshami, Lee, Byungjae, Paeng, Kyunghyun, Zhong, Aoxiao, Li, Quanzheng, Zanjani, Farhad Ghazvinian, Zinger, Svitlana, Fukuta, Keisuke, Komura, Daisuke, Ovtcharov, Vlado, Cheng, Shenghua, Zeng, Shaoqun, Thagaard, Jeppe, Dahl, Anders Bjorholm, Lin, Huangjing, Chen, Hao, Jacobsson, Ludwig, Hedlund, Martin, Cetin, Melih, Halici, Eren, Jackson, Hunter, Chen, Richard, Both, Fabian, Franke, Jorg, Kusters-Vandevelde, Heidi, Vreuls, Willem, Bult, Peter, van Ginneken, Bram, van der Laak, Jeroen, Litjens, Geert, Bandi, Peter, Geessink, Oscar, Manson, Quirine, van Dijk, Marcory, Balkenhol, Maschenka, Hermsen, Meyke, Bejnordi, Babak Ehteshami, Lee, Byungjae, Paeng, Kyunghyun, Zhong, Aoxiao, Li, Quanzheng, Zanjani, Farhad Ghazvinian, Zinger, Svitlana, Fukuta, Keisuke, Komura, Daisuke, Ovtcharov, Vlado, Cheng, Shenghua, Zeng, Shaoqun, Thagaard, Jeppe, Dahl, Anders Bjorholm, Lin, Huangjing, Chen, Hao, Jacobsson, Ludwig, Hedlund, Martin, Cetin, Melih, Halici, Eren, Jackson, Hunter, Chen, Richard, Both, Fabian, Franke, Jorg, Kusters-Vandevelde, Heidi, Vreuls, Willem, Bult, Peter, van Ginneken, Bram, van der Laak, Jeroen, and Litjens, Geert

Abstract

Automated detection of cancer metastases in lymph nodes has the potential to improve assessment of prognosis for patients. To enable fair comparison between the algorithms for this purpose, we set up the CAMELYON17 challenge in conjunction with the IEEE International Symposium on Biomedical Imaging 2017 conference in Melbourne. Over 300 participants registered on the challenge website, of which 23 teams submitted a total of 37 algorithms before the initial deadline. Participants were provided with 899 whole-slide images for developing their algorithms. The developed algorithms were evaluated based on the test set encompassing 100 patients and 500 whole-slide images. The evaluation metric used was a quadratic weighted Cohen’s kappa. We discuss the algorithmic details of the ten best pre-conference and two post-conference submissions. All these participants used convolutional neural networks in combination with pre-and postprocessing steps. Algorithms differed mostly in neural network architecture, training strategy and pre-and postprocessing methodology. Overall, the kappa metric ranged from 0.89 to -0.13 across all submissions. The best results were obtained with pre-trained architectures such as ResNet. Confusion matrix analysis revealed that all participants struggled with reliably identifying isolated tumor cells, the smallest type of metastasis, with detection rates below 40%. Qualitative inspection of the results of the top participants showed categories of false positives, such as nerves or contamination, which could be targets for further optimization. Last, we show that simple combinations of the top algorithms result in higher kappa metric values than any algorithm individually, with 0.93 for the best combination.

Published
2018

35. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Author

Pathologie Groep Van Diest, Pathologie mortuarium en obductie, Pathologie, Cancer, Beeldverwerking ISI, Bejnordi, Babak Ehteshami, Veta, Mitko, Van Diest, Paul Johannes, Van Ginneken, Bram, Karssemeijer, Nico, Litjens, Geert, van der Laak, Jeroen A W M, Hermsen, Meyke, Manson, Quirine F., Balkenhol, Maschenka, Geessink, Oscar, Stathonikos, Nikolaos, van Dijk, Marcory C R F, Bult, Peter, Beca, Francisco, Beck, Andrew H., Wang, Dayong, Khosla, Aditya, Gargeya, Rishab, Irshad, Humayun, Zhong, Aoxiao, Dou, Qi, Li, Quanzheng, Chen, Hao, Lin, Huang Jing, Heng, Pheng-Ann, Haß, Christian, Bruni, Elia, Wong, Quincy, Halici, Ugur, Öner, Mustafa Ümit, Cetin-Atalay, Rengul, Berseth, Matt, Khvatkov, Vitali, Vylegzhanin, Alexei, Kraus, Oren, Shaban, Muhammad, Rajpoot, Nasir M., Awan, Ruqayya, Sirinukunwattana, Korsuk, Qaiser, Talha, Tsang, Yee Wah, Tellez, David, Annuscheit, Jonas, Hufnagl, Peter, Valkonen, Mira, Kartasalo, Kimmo, Latonen, Leena, Ruusuvuori, Pekka, Liimatainen, Kaisa, CAMELYON16 Consortium, Pathologie Groep Van Diest, Pathologie mortuarium en obductie, Pathologie, Cancer, Beeldverwerking ISI, Bejnordi, Babak Ehteshami, Veta, Mitko, Van Diest, Paul Johannes, Van Ginneken, Bram, Karssemeijer, Nico, Litjens, Geert, van der Laak, Jeroen A W M, Hermsen, Meyke, Manson, Quirine F., Balkenhol, Maschenka, Geessink, Oscar, Stathonikos, Nikolaos, van Dijk, Marcory C R F, Bult, Peter, Beca, Francisco, Beck, Andrew H., Wang, Dayong, Khosla, Aditya, Gargeya, Rishab, Irshad, Humayun, Zhong, Aoxiao, Dou, Qi, Li, Quanzheng, Chen, Hao, Lin, Huang Jing, Heng, Pheng-Ann, Haß, Christian, Bruni, Elia, Wong, Quincy, Halici, Ugur, Öner, Mustafa Ümit, Cetin-Atalay, Rengul, Berseth, Matt, Khvatkov, Vitali, Vylegzhanin, Alexei, Kraus, Oren, Shaban, Muhammad, Rajpoot, Nasir M., Awan, Ruqayya, Sirinukunwattana, Korsuk, Qaiser, Talha, Tsang, Yee Wah, Tellez, David, Annuscheit, Jonas, Hufnagl, Peter, Valkonen, Mira, Kartasalo, Kimmo, Latonen, Leena, Ruusuvuori, Pekka, Liimatainen, Kaisa, and CAMELYON16 Consortium

Published
2017

39. Stunting correlates with high salivary and serum antibody levels after 13-valent pneumococcal conjugate vaccination of Venezuelan Amerindian children

Author

Verhagen, Lilly M, Hermsen, Meyke, Rivera-Olivero, Ismar, Sisco, María Carolina, Pinelli, Elena, Hermans, Peter W M, Berbers, Guy A M, de Waard, Jacobus H, de Jonge, Marien I, Verhagen, Lilly M, Hermsen, Meyke, Rivera-Olivero, Ismar, Sisco, María Carolina, Pinelli, Elena, Hermans, Peter W M, Berbers, Guy A M, de Waard, Jacobus H, and de Jonge, Marien I

Published
2016

40. Stunting correlates with high salivary and serum antibody levels after 13-valent pneumococcal conjugate vaccination of Venezuelan Amerindian children

Author

Arts-assistenten Kinderen, Verhagen, Lilly M, Hermsen, Meyke, Rivera-Olivero, Ismar, Sisco, María Carolina, Pinelli, Elena, Hermans, Peter W M, Berbers, Guy A M, de Waard, Jacobus H, de Jonge, Marien I, Arts-assistenten Kinderen, Verhagen, Lilly M, Hermsen, Meyke, Rivera-Olivero, Ismar, Sisco, María Carolina, Pinelli, Elena, Hermans, Peter W M, Berbers, Guy A M, de Waard, Jacobus H, and de Jonge, Marien I

Published
2016

41. Introduction of the 13-valent pneumococcal conjugate vaccine in an isolated pneumococcal vaccine-naïve indigenous population

Author

Arts-assistenten Kinderen, Infectieziekten onderzoek3 (Bogaert), Child Health, Infection & Immunity, Verhagen, Lilly M, Rivera-Olivero, Ismar A, Hermsen, Meyke, Sisco, María Carolina, Maes, Mailis, Del Nogal, Berenice, Bogaert, Debby, Berbers, Guy A M, Hermans, Peter W M, de Jonge, Marien I, de Waard, Jacobus H, Arts-assistenten Kinderen, Infectieziekten onderzoek3 (Bogaert), Child Health, Infection & Immunity, Verhagen, Lilly M, Rivera-Olivero, Ismar A, Hermsen, Meyke, Sisco, María Carolina, Maes, Mailis, Del Nogal, Berenice, Bogaert, Debby, Berbers, Guy A M, Hermans, Peter W M, de Jonge, Marien I, and de Waard, Jacobus H

Published
2016

49. Introduction of the 13-valent pneumococcal conjugate vaccine in an isolated pneumococcal vaccine-naïve indigenous population.

Author

Verhagen LM, Rivera-Olivero IA, Hermsen M, Sisco MC, Maes M, Del Nogal B, Bogaert D, Berbers GA, Hermans PW, de Jonge MI, and de Waard JH

Subjects
Child, Child, Preschool, Humans, Immunoglobulin G blood, Infant, Nasopharynx microbiology, Pneumococcal Infections epidemiology, Population Groups, Pneumococcal Infections prevention & control, Pneumococcal Vaccines immunology, Streptococcus pneumoniae immunology, Vaccines, Conjugate immunology
Published
2016
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