Medical Image Models
Co-Correcting
Introduced by Liu et al. in Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCo-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.
Source: Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Classification | 1 | 25.00% |
| Image Classification | 1 | 25.00% |
| Learning with noisy labels | 1 | 25.00% |
| Medical Image Classification | 1 | 25.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
