Semi-Supervised Disease Classification Based on Limited Medical Image Data
Author:
Affiliation:
1. School of Science, Minzu University of China, Beijing, China
2. Joint Research Center on Computational Mathematics and Control, Shenzhen MSU-BIT University, Shenzhen, China
3. Institute of Data Science, National University of Singapore, Singapore
Funder
Basic and Applied Basic Research Foundation of Guangdong Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6221020/10461324/10381816.pdf?arnumber=10381816
Reference51 articles.
1. Loss Decomposition and Centroid Estimation for Positive and Unlabeled Learning
2. Minimizing Estimated Risks on Unlabeled Data: A New Formulation for Semi-Supervised Medical Image Segmentation
3. Molecular contrastive learning of representations via graph neural networks
4. DSAL: Deeply Supervised Active Learning From Strong and Weak Labelers for Biomedical Image Segmentation
5. Instance-Dependent Positive and Unlabeled Learning with Labeling Bias Estimation
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