Summary of A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model, by Yingxue Xu et al.
A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model
by Yingxue Xu, Yihui Wang, Fengtao Zhou, Jiabo Ma, Shu Yang, Huangjing Lin, Xin Wang, Jiguang Wang, Li Liang, Anjia Han, Ronald Cheong Kin Chan, Hao Chen
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces a task-agnostic foundation model that improves performance across various clinical tasks. By leveraging multimodal data, including pathology reports and gene expression profiles, the proposed model, Multimodal Self-TAught PRetraining (mSTAR), enhances the capabilities of computational pathology models. The authors create a large dataset consisting of whole-slide images and associated reports and RNA-Seq data, which is then used to pretrain mSTAR. This approach enables the model to capture whole-slide patterns, unlike previous patch-level pretraining methods. The paper demonstrates significant performance enhancements for mSTAR across 7 diverse tasks on 43 subtasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a new way to make computers better at looking at pictures of cancer cells. They collect lots of information from doctors and use it to teach the computer to be smarter. The computer gets much better at understanding what it sees, which helps doctors make better decisions. This is important because it can help people get the right treatment for their cancer. |
Keywords
» Artificial intelligence » Pretraining
