Summary of A Study on Self-supervised Pretraining For Vision Problems in Gastrointestinal Endoscopy, by Edward Sanderson and Bogdan J. Matuszewski
A Study on Self-Supervised Pretraining for Vision Problems in Gastrointestinal Endoscopy
by Edward Sanderson, Bogdan J. Matuszewski
First submitted to arxiv on: 11 Jan 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 This paper explores the performance of pre-trained models in various gastrointestinal endoscopy (GIE) vision tasks. The authors investigate the use of self-supervised and supervised pre-training methods, comparing the fine-tuned results on different backbones, including ResNet50 and ViT-B. The study finds that self-supervised pre-training generally outperforms supervised pre-training, with ImageNet-1k as a suitable backbone for most tasks. However, Hyperkvasir-unlabelled is more effective for monocular depth estimation in colonoscopy. Notably, ViT-Bs excel in polyp segmentation and monocular depth estimation, while ResNet50s are better suited for polyp detection. The authors highlight three key principles: self-supervised pre-training typically performs better than supervised pre-training; Hyperkvasir-unlabelled is suitable only for specific tasks; and different architectures perform well in distinct areas. This work aims to inform the development of more suitable approaches for GIE vision tasks, inspiring further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence (AI) can help doctors with a type of medical imaging called gastrointestinal endoscopy. The authors test different ways of training AI models and see which ones work best for specific tasks, like finding certain things in images or judging distances. They find that using AI to learn from itself is usually better than teaching it through labeled data. Some models are better at certain tasks, like recognizing patterns or measuring sizes. The study shows how different approaches can be used for different problems and hopes to help others develop new ways of using AI for medical imaging. |
Keywords
* Artificial intelligence * Depth estimation * Self supervised * Supervised * Vit
