Summary of A Self-supervised Framework For Learning Whole Slide Representations, by Xinhai Hou et al.
A self-supervised framework for learning whole slide representations
by Xinhai Hou, Cheng Jiang, Akhil Kondepudi, Yiwei Lyu, Asadur Chowdury, Honglak Lee, Todd C. Hollon
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 novel self-supervised learning method, Slide Pre-trained Transformers (SPT), designed specifically for gigapixel-sized whole slide images (WSIs) in biomedical microscopy. SPT combines data transformation strategies from language and vision modeling to generate views of WSIs, leveraging the inherent regional heterogeneity, histologic feature variability, and information redundancy within WSIs to learn high-quality whole slide representations. The authors benchmark SPT visual representations on five diagnostic tasks across three biomedical microscopy datasets, demonstrating significant performance improvements compared to baselines for histopathologic diagnosis, cancer subtyping, and genetic mutation prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine taking a picture of a tiny piece of tissue under a microscope. This paper is about creating a new way to analyze these big pictures (called whole slide images) without having to label every single detail. The method, called Slide Pre-trained Transformers, helps computers learn from the patterns and features within these images, which can be very useful for diagnosing diseases or predicting patient outcomes. |
Keywords
* Artificial intelligence * Self supervised
