Summary of Towards Large-scale Training Of Pathology Foundation Models, by Kaiko.ai et al.
Towards Large-Scale Training of Pathology Foundation Models
by kaiko.ai, Nanne Aben, Edwin D. de Jong, Ioannis Gatopoulos, Nicolas Känzig, Mikhail Karasikov, Axel Lagré, Roman Moser, Joost van Doorn, Fei Tang
First submitted to arxiv on: 24 Mar 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 presents a scalable training pipeline for building foundation models (FMs) for medical images, specifically focusing on pathology imaging data. The authors demonstrate their approach by releasing publicly available FMs trained on open-access TCGA whole slide images and evaluating their performance on various patch-level downstream tasks, including breast cancer subtyping and colorectal nuclear segmentation. Their models reach state-of-the-art performance in these tasks, showcasing the potential of self-supervised learning algorithms for medical image analysis. The authors also introduce an open-source framework designed to unify evaluation approaches across different FMs and downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve how computers analyze medical images by creating special models called foundation models (FMs). These FMs can help doctors diagnose diseases more accurately. The scientists developed a way to train these models using a large collection of open-access medical images. They tested their approach and found that it worked well for various tasks, such as identifying cancer types or segmenting specific parts of the colon. The team also created an easy-to-use framework for evaluating FMs, which can help other researchers compare their results. |
Keywords
» Artificial intelligence » Self supervised
