Summary of Contrastive-based Deep Embeddings For Label Noise-resilient Histopathology Image Classification, by Lucas Dedieu et al.
Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image Classification
by Lucas Dedieu, Nicolas Nerrienet, Adrien Nivaggioli, Clara Simmat, Marceau Clavel, Arnaud Gauthier, Stéphane Sockeel, Rémy Peyret
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper investigates the impact of noisy labels on deep learning models in histopathology image classification. The authors demonstrate that foundation models trained using self-supervised contrastive learning exhibit improved resilience to label noise, outperforming traditional approaches. This breakthrough could significantly enhance model performance and accuracy in medical imaging applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers explored how noisy labels affect deep learning models in medical image classification. They found that training with embeddings from foundation models can help make models more robust against noisy labels. This discovery has important implications for improving the reliability of medical image analysis. |
Keywords
» Artificial intelligence » Deep learning » Image classification » Self supervised
