A survey on the interpretability of deep learning in medical diagnosis-Reference-Cited by-同舟云学术

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A survey on the interpretability of deep learning in medical diagnosis

Published:2022-06-25 Issue:6 Volume:28 Page:2335-2355
ISSN:0942-4962
Container-title:Multimedia Systems
language:en
Short-container-title:Multimedia Systems

Author:

Teng Qiaoying,Liu Zhe,Song Yuqing,Han Kai,Lu Yang

Funder

National Natural Science Foundation of China

Postdoctoral Science Foundation of Jiangsu Province

Six Talent Peaks Project in Jiangsu Province

the Key Special Project of Health and Family Planning Science and Technology in Zhenjiang City

Innovation capacity building Foundation of Jilin Provincial Development and Reform Commission

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s00530-022-00960-4.pdf

Reference111 articles.

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2. Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., Gao, J.: Deep learning-based text classification: a comprehensive review. ACM Comput. Surv. 54(3), 1–40 (2021). https://doi.org/10.1145/3439726

3. Chrysostomou, G., Aletras, N.: Improving the faithfulness of attention-based explanations with task-specific information for text classification (2021). at preprint arxiv:2105.02657

4. Schwartz, E., Giryes, R., Bronstein, A.M.: Deepisp: Toward learning an end-to-end image processing pipeline. IEEE Trans. Image Process. 28(2), 912–923 (2018). https://doi.org/10.1109/TIP.2018.2872858

5. Sun, J., Darbehani, F., Zaidi, M., Wang, B.: Saunet: Shape attentive u-net for interpretable medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 797–806 (2020) https://doi.org/10.1007/978-3-030-59719-1_77.Springer

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