Summary of Polyp Segmentation Generalisability Of Pretrained Backbones, by Edward Sanderson and Bogdan J. Matuszewski
Polyp Segmentation Generalisability of Pretrained Backbones
by Edward Sanderson, Bogdan J. Matuszewski
First submitted to arxiv on: 24 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 paper explores the generalizability of pre-trained models for polyp segmentation. Recent studies have shown that self-supervised pretraining improves fine-tuned performance, and models with Vision Transformer (ViT-B) backbones outperform those with ResNet50 backbones. This study assesses how well these models generalize to new data distributions, which is crucial in real-world deployment scenarios. The results confirm the previous findings on pre-training pipelines for polyp segmentation, but surprisingly suggest that ResNet50-based models generalize better despite being outperformed by ViT-B models on the same test set. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well AI models work when they’re tested with new data. It’s like trying a recipe you found online and seeing if it works in your own kitchen. The study shows that pre-training AI models makes them better at recognizing polyps, and models using certain types of “backbones” (like a skeleton for the model) do better than others. But here’s the cool thing: even though some models are good at recognizing polyps on one set of data, they’re actually better at generalizing to new data when using different backbones. |
Keywords
» Artificial intelligence » Pretraining » Self supervised » Vision transformer » Vit
