From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge von Bandi, Peter, Geessink, Oscar, Manson, Quirine, Van Dijk, Marcory, Balkenhol, Maschenka, Hermsen, Meyke, Ehteshami Bejnordi, Babak, 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 B., 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

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Stain Specific Standardization of Whole-Slide Histopathological Images von Ehteshami Bejnordi, Babak, Litjens, Geert, Timofeeva, Nadya, Otte-Holler, Irene, Homeyer, Andre, Karssemeijer, Nico, van der Laak, Jeroen Awm

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1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset von Litjens, Geert, Bandi, Peter, Ehteshami Bejnordi, Babak, 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

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Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies von Ehteshami Bejnordi, Babak, Mullooly, Maeve, Pfeiffer, Ruth M., Fan, Shaoqi, Vacek, Pamela M., Weaver, Donald L., Herschorn, Sally, Brinton, Louise A., van Ginneken, Bram, Karssemeijer, Nico, Beck, Andrew H., Gierach, Gretchen L., van der Laak, Jeroen A. W.M., Sherman, Mark E.

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Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images von Ehteshami Bejnordi, Babak, Balkenhol, Maschenka, Litjens, Geert, Holland, Roland, Bult, Peter, Karssemeijer, Nico, van der Laak, Jeroen A. W. M.

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Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer von Geessink, Oscar G. F., Baidoshvili, Alexi, Klaase, Joost M., Ehteshami Bejnordi, Babak, Litjens, Geert J. S., van Pelt, Gabi W., Mesker, Wilma E., Nagtegaal, Iris D., Ciompi, Francesco, van der Laak, Jeroen A. W. M.

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1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset von Litjens, Geert, Bandi, Peter, Ehteshami Bejnordi, Babak, 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

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Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer von 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, Venâncio, Rui

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Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density von Mullooly, Maeve, Ehteshami Bejnordi, Babak, Pfeiffer, Ruth M., Fan, Shaoqi, Palakal, Maya, Hada, Manila, Vacek, Pamela M., Weaver, Donald L., Shepherd, John A., Fan, Bo, Mahmoudzadeh, Amir Pasha, Wang, Jeff, Malkov, Serghei, Johnson, Jason M., Herschorn, Sally D., Sprague, Brian L., Hewitt, Stephen, Brinton, Louise A., Karssemeijer, Nico, van der Laak, Jeroen, Beck, Andrew, Sherman, Mark E., Gierach, Gretchen L.

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Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images von Bejnordi, Babak Ehteshami, Zuidhof, Guido, Balkenhol, Maschenka, Hermsen, Meyke, Bult, Peter, van Ginneken, Bram, Karssemeijer, Nico, Litjens, Geert, van der Laak, Jeroen

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Computational pathology: A survey review and the way forward von Hosseini, Mahdi S., Bejnordi, Babak Ehteshami, Trinh, Vincent Quoc-Huy, Chan, Lyndon, Hasan, Danial, Li, Xingwen, Yang, Stephen, Kim, Taehyo, Zhang, Haochen, Wu, Theodore, Chinniah, Kajanan, Maghsoudlou, Sina, Zhang, Ryan, Zhu, Jiadai, Khaki, Samir, Buin, Andrei, Chaji, Fatemeh, Salehi, Ala, Nguyen, Bich Ngoc, Samaras, Dimitris, Plataniotis, Konstantinos N.

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3D volume reconstruction from serial breast specimen radiographs for mapping between histology and 3D whole specimen imaging von Mertzanidou, Thomy, Hipwell, John H., Reis, Sara, Hawkes, David J., Ehteshami Bejnordi, Babak, Dalmis, Mehmet, Vreemann, Suzan, Platel, Bram, Laak, Jeroen, Karssemeijer, Nico, Hermsen, Meyke, Bult, Peter, Mann, Ritse

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Novel chromatin texture features for the classification of Pap smears von Ehteshami Bejnordi, B., Moshavegh, Ramin, Sujathan, K., Malm, P., Bengtsson, Ewert, Mehnert, Andrew

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A fully automatic unsupervised segmentation framework for the brain tissues in MR images von Qaiser, Mahmood, Chodorowski, Artur, Ehteshami Bejnordi, B., Persson, Mikael

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SALISA: Saliency-Based Input Sampling for Efficient Video Object Detection von Ehteshami Bejnordi, Babak, Habibian, Amirhossein, Porikli, Fatih, Ghodrati, Amir

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A survey on deep learning in medical image analysis von Litjens, Geert, Kooi, Thijs, Bejnordi, Babak Ehteshami, Setio, Arnaud Arindra Adiyoso, Ciompi, Francesco, Ghafoorian, Mohsen, van der Laak, Jeroen A.W.M., van Ginneken, Bram, Sánchez, Clara I.

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Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge von Veta, Mitko, Heng, Yujing J., Stathonikos, Nikolas, Bejnordi, Babak Ehteshami, Beca, Francisco, Wollmann, Thomas, Rohr, Karl, Shah, Manan A., Wang, Dayong, Rousson, Mikael, Hedlund, Martin, Tellez, David, Ciompi, Francesco, Zerhouni, Erwan, Lanyi, David, Viana, Matheus, Kovalev, Vassili, Liauchuk, Vitali, Phoulady, Hady Ahmady, Qaiser, Talha, Graham, Simon, Rajpoot, Nasir, Sjöblom, Erik, Molin, Jesper, Paeng, Kyunghyun, Hwang, Sangheum, Park, Sunggyun, Jia, Zhipeng, Chang, Eric I-Chao, Xu, Yan, Beck, Andrew H., van Diest, Paul J., Pluim, Josien P.W.

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Machine Learning Compared With Pathologist Assessment—Reply von Bejnordi, Babak Ehteshami, Litjens, Geert, van der Laak, Jeroen AWM

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Automated segmentation of free-lying cell nuclei in Pap smears for malignancy-associated change analysis von Moshavegh, R., Bejnordi, Babak Ehteshami, Mehnert, A., Sujathan, K., Malm, P., Bengtsson, E.

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Abstract 4235: Application of convolutional neural networks to breast biopsies to uncover tissue correlates of mammographic breast density von Mullooly, Maeve, Bejnordi, Babak Ehteshami, Palakal, Maya, Vacek, Pamela M., Weaver, Donald L., Shepherd, John A., Fan, Bo, Mahmoudzadeh, Amir Pasha, Wang, Jeff, Johnson, Jason M., Herschorn, Sally D., Sprague, Brian L., Pfeiffer, Ruth M., Brinton, Louise A., Sherman, Mark E., Beck, Andrew, Gierach, Gretchen L.

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