How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?-Reference-Cited by-同舟云学术

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How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?

Published:2023 Issue: Volume: Page:663-673
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Holste Gregory,Jiang Ziyu,Jaiswal Ajay,Hanna Maria,Minkowitz Shlomo,Legasto Alan C.,Escalon Joanna G.,Steinberger Sharon,Bittman Mark,Shen Thomas C.,Ding Ying,Summers Ronald M.,Shih George,Peng Yifan,Wang Zhangyang

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-43904-9_64

Reference34 articles.

1. Chen, L., Zhao, L., Chen, C.Y.C.: Enhancing adversarial defense for medical image analysis systems with pruning and attention mechanism. Med. Phys. 48(10), 6198–6212 (2021)

2. Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: ICML, pp. 233–240 (2006)

3. Dinsdale, N.K., Jenkinson, M., Namburete, A.I.: Stamp: simultaneous training and model pruning for low data regimes in medical image segmentation. Med. Image Anal. 81, 102583 (2022)

4. Fernandes, F.E., Yen, G.G.: Automatic searching and pruning of deep neural networks for medical imaging diagnostic. IEEE Trans. Neural Netw. Learn. Syst. 32(12), 5664–5674 (2020)

5. Fernández, A., García, S., Galar, M., Prati, R.C., Krawczyk, B., Herrera, F.: Learning from Imbalanced Data Sets. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98074-4

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