Improving the Sensitivity of Unbalanced Medical Image Classification by Random Up-sampling Numerical Features-Reference-Cited by-同舟云学术

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Improving the Sensitivity of Unbalanced Medical Image Classification by Random Up-sampling Numerical Features

Published:2024-04-26 Issue: Volume:42 Page:15-21
ISSN:
Container-title:Proceedings of the 2024 6th International Conference on Intelligent Medicine and Image Processing
language:
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Author:

Yoshitsugu Kenji¹^ORCID,Kishimoto Kazumasa²^ORCID,Takemura Tadamasa¹^ORCID

Affiliation:

1. Information Science, Graduate School of Information Science University of Hyogo, Japan

2. Division of Medical Information Technology and Administration Planning, Kyoto University Hospital, Japan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3669828.3669831

Reference40 articles.

1. Litjens, G., Kooi, Sánchez, C, (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88.

2. Medical Image Analysis using Convolutional Neural Networks;Qayyum A.;Journal of Medical Systems,2017

3. Shen, D., Wu, G., & Suk, H. (2017). Deep Learning in Medical Image Analysis. Annual review of biomedical engineering, 19, 221-248.

4. Kermany, D., Kermany, D., Zhang, K, (2018). Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell, p. 172, 1122-1131.e9.

5. Deep learning for cellular image analysis

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