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Being able to learn on weakly labeled data, and provide interpretability, are two of the main reasons why attention-based deep multiple instance learning (ABMIL) methods have become particularly popular for classification of histopathological images.
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Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin, · 2016
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Maximilian Ilse, Jakub Tomczak, and Max Welling, · 2018
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Veronika Cheplygina, Marleen de Bruijne, and Josien PW Pluim, · 2019
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Olivier Dehaene, Axel Camara, Olivier Moindrot, Axel de Lavergne, and Pierre Courtiol, · 2020
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Philip Chikontwe, Meejeong Kim, Soo Jeong Nam, Heounjeong Go, and Sang Hyun Park, · 2020
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“Beyond classification: Whole slide tissue histopathology analysis by end-to-end part learning,”
Chensu Xie, Hassan Muhammad, Chad M Vanderbilt, Raul Caso, Dig Vijay Kumar Yarlagadda, Gabriele Campanella, and Thomas J Fuchs, · 2020
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“Data-efficient and weakly supervised computational pathology on whole-slide images,”
Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Richard J Chen, Matteo Barbieri, and Faisal Mahmood, · 2021
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“Pan-cancer integrative histology-genomic analysis via interpretable multimodal deep learning,”
Richard J Chen, Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Jana Lipkova, Muhammad Shaban, Maha Shady, Mane Williams, Bumjin Joo, Zahra Noor, et al., · 2021
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Bin Li, Yin Li, and Kevin W Eliceiri, · 2021
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“The effect of within-bag sampling on end-to-end multiple instance learning,”
Nadezhda Koriakina, Nataša Sladoje, and Joakim Lindblad, · 2021
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Hans Pinckaers, Bram van Ginneken, and Geert Litjens, · 2020
Cited alongside, same era.
“Albumentations: Fast and flexible image augmentations,”
Alexander Buslaev et al., · 2020
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“Imagewang,” https://github.com/fastai/imagenette/
Jeremy Howard,
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“Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels,”
Hans Pinckaers, Wouter Bulten, Jeroen van der Laak, and Geert Litjens, · 2021
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