Summary of Domain-adaptive Pre-training Of Self-supervised Foundation Models For Medical Image Classification in Gastrointestinal Endoscopy, by Marcel Roth et al.
Domain-Adaptive Pre-training of Self-Supervised Foundation Models for Medical Image Classification in Gastrointestinal Endoscopy
by Marcel Roth, Micha V. Nowak, Adrian Krenzer, Frank Puppe
First submitted to arxiv on: 21 Oct 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 abstract presents a novel large-scale gastrointestinal endoscopy (GIE) dataset, called EndoExtend24, which combines ten existing public and private datasets to create over 226,000 labeled images. The dataset includes dynamic class mappings that allow unified training across datasets with differing labeling granularity, supporting up to 123 distinct pathological findings. To analyze these images efficiently, the authors propose leveraging domain adaptive pre-training of foundation models trained on generic image data, adapting them to the task of GIE medical image diagnosis. They use the EVA-02 model, based on the ViT architecture and trained on ImageNet-22k with masked image modeling (using EVA-CLIP as a MIM teacher), which achieves robust performance in the Capsule Endoscopy 2024 Challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a huge database of images from the gastrointestinal tract to help doctors detect diseases earlier. The problem is that there are millions of images, and it takes hours to look at them all, so they need computers to do the job. The team created this big dataset by combining lots of smaller ones, which helps doctors train better computer models. They also used a special training method to teach these models how to recognize different diseases in the images. This work is important because it can help us find and treat diseases earlier, when they are easier to cure. |
Keywords
» Artificial intelligence » Vit
