Methods › Computer Vision › Medical Image Models › Co-Correcting
Co-Correcting
Introduced by Jiarun Liu et al. in Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Co-Correcting is a noise-tolerant deep learning framework for medical image classification based on mutual learning and annotation correction. It consists of three modules: the dual-network architecture, the curriculum learning module, and the label correction module.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction 11 Sep 2021 · 2 repositories · arXiv:2109.05159
Tasks archive 2025-07-28
6 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Classification | 1 |
| Deep Learning | 1 |
| Image Classification | 1 |
| Learning with noisy labels | 1 |
| Medical Image Classification | 1 |
| image-classification | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections