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Deep learning for histopathology has been successfully used for disease classification, image segmentation and more.
“Bleu: a method for automatic evaluation of machine translation,”
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu, · 2002
Earlier work this paper cites.
“Rouge: A package for automatic evaluation of summaries,”
Chin-Yew Lin, · 2004
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“Meteor: An automatic metric for mt evaluation with improved correlation with human judgments,”
Satanjeev Banerjee and Alon Lavie, · 2005
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“Densely connected convolutional networks,”
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger, · 2017
Earlier work this paper cites.
“Efficientnet: Rethinking model scaling for convolutional neural networks,”
Mingxing Tan and Quoc Le, · 2019
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“Google, kt, language, ai: Bert: pre-training of deep bidirectional transformers for language understanding,”
Jacob Devlin, Ming-Wei Chang, and Kenton Lee, · 2019
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“Publicly available clinical bert embeddings,”
Emily Alsentzer, John R Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew McDermott, · 2019
Cited alongside, same era.
“Feature difference makes sense: a medical image captioning model exploiting feature difference and tag information,”
Hyeryun Park, Kyungmo Kim, Jooyoung Yoon, Seongkeun Park, and Jinwook Choi, · 2020
Cited alongside, same era.
“Evaluating and interpreting caption prediction for histopathology images,”
Renyu Zhang, Christopher Weber, Robert Grossman, and Aly A Khan, · 2020
Cited alongside, same era.
“Multiple instance captioning: Learning representations from histopathology textbooks and articles,”
Jevgenij Gamper and Nasir Rajpoot, · 2021
Cited alongside, same era.
“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, Tai-I Hsu, Michael Hsiao, Chao-Yuan Yeh, and Cheng-Yu Chen, · 2021
“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
Later among the works it cites.
“Inference of captions from histopathological patches,”
Masayuki Tsuneki and Fahdi Kanavati, · 2022
Later among the works it cites.
“Scaling vision transformers to gigapixel images via hierarchical self-supervised learning,”
Richard J Chen, Chengkuan Chen, Yicong Li, Tiffany Y Chen, Andrew D Trister, Rahul G Krishnan, and Faisal Mahmood, · 2022
Later among the works it cites.
“Trocr: Transformer-based optical character recognition with pre-trained models,”
Minghao Li, Tengchao Lv, Jingye Chen, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, and Furu Wei, · 2023
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Keith Harrigian, Tina Tang, Anthony Gonzales, Cindy X Cai, and Mark Dredze, · 2023
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Cited alongside, same era.
“Emerging properties in self-supervised vision transformers,”
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin, · 2021
Cited alongside, same era.
Closest in time.
“Show, attend and tell: Neural image caption generation with visual attention,”
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio, · 2057
Closest in time.