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In the field of computational pathology, the use of decision support systems powered by state-of-the-art deep learning solutions has been hampered by the lack of large labeled datasets.
Prostatic carcinoma reproducibility of histologic grading
Hans Svanholm and Henrik Mygind · 1985
Earlier work this paper cites.
Multiple instance learning with generalized support vector machines
Stuart Andrews, Thomas Hofmann, and Ioannis Tsochantaridis · 2002
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Em-dd: An improved multiple-instance learning technique
Qi Zhang and Sally A Goldman · 2002
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Multiple instance boosting for object detection
Cha Zhang, John C Platt, and Paul A Viola · 2006
Cited alongside, same era.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Cited alongside, same era.
OpenSlide: A vendor-neutral software foundation for digital pathology
Adam. Goode, Benjamin. Gilbert, Jan. Harkes, Drazen. Jukic, and Mahadev. Satyanarayanan · 2013
Cited alongside, same era.
Solving the multiple instance problem with axis-parallel rectangles
Thomas G. Dietterich, Richard H. Lathrop, and Tomás Lozano-Pérez
Cited in the paper.
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes van Diest, Bram van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen A. W. M. van der Laak, and the CAMELYON16 Consortium, Meyke Hermsen, Quirine F Manson, Maschenka Balkenhol, Oscar Geessink, Nikolaos Stathonikos, Marcory CRF van Dijk, Peter Bult, Francisco Beca, Andrew H Beck, Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, Aoxiao Zhong, Qi Dou, Quanzheng Li, Hao Chen, Huang-Jing Lin, Pheng-Ann Heng, Christian Haß, Elia Bruni, Quincy Wong, Ugur Halici, Mustafa Ümit Öner, Rengul Cetin-Atalay, Matt Berseth, Vitali Khvatkov, Alexei Vylegzhanin, Oren Kraus, Muhammad Shaban, Nasir Rajpoot, Ruqayya Awan, Korsuk Sirinukunwattana, Talha Qaiser, Yee-Wah Tsang, David Tellez, Jonas Annuscheit, Peter Hufnagl, Mira Valkonen, Kimmo Kartasalo, Leena Latonen, Pekka Ruusuvuori, Kaisa Liimatainen, Shadi Albarqouni, Bharti Mungal, Ami George, Stefanie Demirci, Nassir Navab, Seiryo Watanabe, Shigeto Seno, Yoichi Takenaka, Hideo Matsuda, Hady Ahmady Phoulady, Vassili Kovalev, Alexander Kalinovsky, Vitali Liauchuk, Gloria Bueno, M. Milagro Fernandez-Carrobles, Ismael Serrano, Oscar Deniz, Daniel Racoceanu, and Rui Venâncio
Cited in the paper.
Computational pathology: Challenges and promises for tissue analysis
Thomas J. Fuchs and Joachim M. Buhmann
Cited in the paper.
Computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients
Thomas J. Fuchs, Peter J. Wild, Holger Moch, and Joachim M. Buhmann
Cited in the paper.
Histologic grading of prostate cancer: a perspective
Donald F. Gleason
Cited in the paper.
Patch-based convolutional neural network for whole slide tissue image classification
Le Hou, Dimitris Samaras, Tahsin M. Kurc, Yi Gao, James E. Davis, and Joel H. Saltz
Cited in the paper.
Detecting cancer metastases on gigapixel pathology images
Yun Liu, Krishna Gadepalli, Mohammad Norouzi, George E. Dahl, Timo Kohlberger, Aleksey Boyko, Subhashini Venugopalan, Aleksei Timofeev, Philip Q. Nelson, and Greg S. Corrado
Cited in the paper.
Learning from data with low intrinsic dimension
Nakul Verma
Cited in the paper.
Deep learning of feature representation with multiple instance learning for medical image analysis
Yan Xu, Tao Mo, Qiwei Feng, Peilin Zhong, Maode Lai, and Eric I-Chao Chang
Cited in the paper.
Cancer statistics, 2016: Cancer statistics, 2016
Rebecca L. Siegel, Kimberly D. Miller, and Ahmedin Jemal · 2016
Later among the works it cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Later among the works it cites.
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