Summary of Exaonepath 1.0 Patch-level Foundation Model For Pathology, by Juseung Yun et al.
EXAONEPath 1.0 Patch-level Foundation Model for Pathology
by Juseung Yun, Yi Hu, Jinhyung Kim, Jongseong Jang, Soonyoung Lee
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 presents a novel foundational model called EXAONEPath, designed for digital pathology tasks. The model is trained on patches from whole slide images (WSIs) with stain normalization to reduce color variability. This approach helps mitigate WSI-specific feature collapse, which can limit the model’s generalization ability and performance. The authors compare EXAONEPath with state-of-the-art models across six downstream task datasets, demonstrating superior performance relative to the number of WSIs used and the model’s parameter count. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new tool for analyzing medical images called EXAONEPath. This tool helps computers better understand images by reducing differences caused by different labs and scanners. The authors tested this tool on many image patches and showed that it works better than other tools, even when using fewer images and less computer power. |
Keywords
» Artificial intelligence » Generalization
