Summary of Evaluating Self-supervised Learning in Medical Imaging: a Benchmark For Robustness, Generalizability, and Multi-domain Impact, by Valay Bundele et al.
Evaluating Self-Supervised Learning in Medical Imaging: A Benchmark for Robustness, Generalizability, and Multi-Domain Impact
by Valay Bundele, Oğuz Ata Çal, Bora Kargi, Karahan Sarıtaş, Kıvanç Tezören, Zohreh Ghaderi, Hendrik Lensch
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper presents a comprehensive evaluation of self-supervised learning (SSL) methods in the medical imaging domain, focusing on robustness and generalizability. Eight major SSL methods are evaluated across 11 medical datasets using the MedMNIST dataset collection as a benchmark. The study analyzes model performance in both in-domain scenarios and out-of-distribution sample detection, exploring initialization strategies, architectures, and multi-domain pre-training effects. Additionally, cross-dataset evaluations and simulations of real-world scenarios with limited supervision (1%, 10%, and 100% label proportions) are conducted to assess generalizability. This benchmark aims to provide practitioners and researchers with a valuable tool for informed decision-making when applying SSL methods to medical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Researchers are working on a new way to teach machines to learn from images without needing labels, which is important in the medical field where there’s often not enough labeled data. This paper looks at how well these self-supervised learning (SSL) methods work across different medical datasets and scenarios. They tested 8 popular SSL methods on 11 medical image datasets and found that some methods do better than others depending on the situation. The study also looked at how well the models generalize to new, unseen images. The goal is to help people make informed decisions when using these SSL methods in medicine. |
Keywords
» Artificial intelligence » Self supervised
