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Strongly supervised learning requires detailed knowledge of truth labels at instance levels, and in many machine learning applications this is a major drawback.
“A framework for multiple-instance learning,”
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“Gradient-based learning applied to document recognition,”
Y. LeCun, L. Bottou, Yoshua Bengio, and P. Haffner, · 1998
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“Multiple instance classification: Review, taxonomy and comparative study,”
J. Amores, · 2013
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“Bag of bags: Nested multi instance classification for prostate cancer detection,”
F. Khalvati, Junjie Zhang, A. Wong, and M. Haider, · 2016
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“Tensorflow: A system for large-scale machine learning,”
Martín Abadi, Paul Barham, Jianmin Chen, Z. Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zhang, · 2016
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“Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer,”
Babak Ehteshami Bejnordi, M. Veta, Paul Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. van der Laak, M. Hermsen, Quirine F Manson, and Maschenka C. A. Balkenhol et al, · 2017
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“A network architecture for multi-multi-instance learning,”
Alessandro Tibo, P. Frasconi, and M. Jaeger, · 2017
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“Explaining explanations: An overview of interpretability of machine learning,”
Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael A. Specter, and Lalana Kagal, · 2018
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“Attention-based deep multiple instance learning,”
Maximilian Ilse, Jakub M. Tomczak, and M. Welling, · 2018
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“Rotation equivariant cnns for digital pathology,”
Bastiaan S. Veeling, J. Linmans, Jim Winkens, T. Cohen, and M. Welling, · 2018
Cited alongside, same era.
“Multiple instance learning: A survey of problem characteristics and applications,”
M. Carbonneau, V. Cheplygina, Eric Granger, and G. Gagnon, · 2018
Cited alongside, same era.
“An improved approach to weakly supervised semantic segmentation,”
Lian Xu, M. Bennamoun, Farid Boussaïd, S. An, and Ferdous Sohel, · 2019
Cited alongside, same era.
“Visual entailment: A novel task for fine-grained image understanding,”
Ning Xie, Farley Lai, Derek Doran, and Asim Kadav, · 2019
Cited alongside, same era.
“Explainable deep learning: A field guide for the uninitiated,”
Ning Xie, Gabrielle Ras, M. V. Gerven, and Derek Doran, · 2020
“Learning and interpreting multi-multi-instance learning networks,”
Alessandro Tibo, M. Jaeger, and P. Frasconi, · 2020
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“Channel attention residual u-net for retinal vessel segmentation,”
Changlu Guo, Marton Szemenyei, Yugen Yi, and W. Zhou, · 2021
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“An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning,”
Chi-Long Chen, Chi-Chung Chen, Wei-Hsiang Yu, Szu-Hua Chen, Yu-Chan Chang, T. Hsu, M. Hsiao, Chao-Yuan Yeh, and Cheng yu Chen, · 2021
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“Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning,”
Bin Li, Yin Li, and K. Eliceiri, · 2021
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“Data efficient and weakly supervised computational pathology on whole slide images,”
M. Lu, Drew F. K. Williamson, Tiffany Y Chen, Richard J. Chen, Matteo Barbieri, and Faisal Mahmood, · 2021
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“Arnet: Attention-based refinement network for few-shot semantic segmentation,”
Rusheng Li, Hanhui Liu, Yuesheng Zhu, and Zhiqiang Bai, · 2020
Cited alongside, same era.
“Weakly labelled audio tagging via convolutional networks with spatial and channel-wise attention,”
Sixin Hong, Yuexian Zou, Wenwu Wang, and Meng Cao, · 2020
Cited alongside, same era.
“Multiple instance learning with center embeddings for histopathology classification,”
P. Chikontwe, Meejeong Kim, S. Nam, H. Go, and Sang Hyun Park, · 2020
Cited alongside, same era.
“Clustering-based multiple instance learning with multi-view feature,”
Chengkun He, Jie Shao, Jiasheng Zhang, and Xiangmin Zhou, · 2020
Cited alongside, same era.
“A multi-resolution model for histopathology image classification and localization with multiple instance learning,”
Jiayun Li, Wenyuan Li, A. Sisk, H. Ye, W. Wallace, W. Speier, and C. Arnold, · 2021
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“Automatic feature selection for improved interpretability on whole slide imaging,”
Antoine Pirovano, Hippolyte Heuberger, S. Berlemont, Saïd Ladjal, and I. Bloch, · 2021
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Yash Sharma, A. Shrivastava, L. Ehsan, C. Moskaluk, S. Syed, and Donald E. Brown, · 2021
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