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Since the era of deep learning, convolutional neural networks (CNNs) and vision transformers (ViTs) have been extensively studied and widely used in medical image classification tasks.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Phoneme recognition using time-delay neural networks
Alexander Waibel, Toshiyuki Hanazawa, Geoffrey Hinton, Kiyohiro Shikano, and Kevin J Lang · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
Earlier work this paper cites.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Earlier work this paper cites.
Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Earlier work this paper cites.
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Konstantin Pogorelov, Kristin Ranheim Randel, Carsten Griwodz, Sigrun Losada Eskeland, Thomas de Lange, Dag Johansen, Concetto Spampinato, Duc-Tien Dang-Nguyen, Mathias Lux, Peter Thelin Schmidt, et al · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Earlier work this paper cites.
Medical imaging: from roentgen to the digital revolution, and beyond
Eyal Bercovich and Marcia C Javitt · 2018
Earlier work this paper cites.
Large-scale retrieval for medical image analytics: A comprehensive review
Zhongyu Li, Xiaofan Zhang, Henning Müller, and Shaoting Zhang · 2018
Earlier work this paper cites.
Recent advances in convolutional neural networks
Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Gang Wang, Jianfei Cai, et al · 2018
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Abien Fred Agarap · 2018
Earlier work this paper cites.
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Stefan Elfwing, Eiji Uchibe, and Kenji Doya · 2018
Earlier work this paper cites.
Deep convolutional neural network based medical image classification for disease diagnosis
Samir S Yadav and Shivajirao M Jadhav · 2019
Earlier work this paper cites.
Medical image classification using synergic deep learning
Jianpeng Zhang, Yutong Xie, Qi Wu, and Yong Xia · 2019
Earlier work this paper cites.
Autoaugment: Learning augmentation strategies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
Earlier work this paper cites.
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Mingxing Tan and Quoc Le · 2019
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Haifeng Jin, Qingquan Song, and Xia Hu · 2019
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Ekaba Bisong and Ekaba Bisong · 2019
Earlier work this paper cites.
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Hiroshi Fujita · 2020
Earlier work this paper cites.
Artificial intelligence in health care: Current applications and issues
Chan-Woo Park, Sung Wook Seo, Noeul Kang, BeomSeok Ko, Byung Wook Choi, Chang Min Park, Dong Kyung Chang, Hwiuoung Kim, Hyunchul Kim, Hyunna Lee, et al · 2020
Earlier work this paper cites.
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Weibin Wang, Dong Liang, Qingqing Chen, Yutaro Iwamoto, Xian-Hua Han, Qiaowei Zhang, Hongjie Hu, Lanfen Lin, and Yen-Wei Chen · 2020
Earlier work this paper cites.
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Rehan Ashraf, Muhammad Asif Habib, Muhammad Akram, Muhammad Ahsan Latif, Muhammad Sheraz Arshad Malik, Muhammad Awais, Saadat Hanif Dar, Toqeer Mahmood, Muhammad Yasir, and Zahoor Abbas · 2020
Earlier work this paper cites.
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Yuan Liu, Ayush Jain, Clara Eng, David H Way, Kang Lee, Peggy Bui, Kimberly Kanada, Guilherme de Oliveira Marinho, Jessica Gallegos, Sara Gabriele, et al · 2020
Earlier work this paper cites.
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Earlier work this paper cites.
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Andre GC Pacheco, Gustavo R Lima, Amanda S Salomao, Breno Krohling, Igor P Biral, Gabriel G de Angelo, Fábio CR Alves Jr, José GM Esgario, Alana C Simora, Pedro BC Castro, et al · 2020
Earlier work this paper cites.
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Xavier P Burgos-Artizzu, David Coronado-Gutiérrez, Brenda Valenzuela-Alcaraz, Elisenda Bonet-Carne, Elisenda Eixarch, Fatima Crispi, and Eduard Gratacós · 2020
Earlier work this paper cites.
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Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Earlier work this paper cites.
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Stéphane d’Ascoli, Hugo Touvron, Matthew L Leavitt, Ari S Morcos, Giulio Biroli, and Levent Sagun · 2021
Earlier work this paper cites.
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Earlier work this paper cites.
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