Summary of Segmentation by Factorization: Unsupervised Semantic Segmentation For Pathology By Factorizing Foundation Model Features, By Jacob Gildenblat et al.
Segmentation by Factorization: Unsupervised Semantic Segmentation for Pathology by Factorizing Foundation Model Features
by Jacob Gildenblat, Ofir Hadar
First submitted to arxiv on: 9 Sep 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 Segmentation by Factorization (F-SEG) is an unsupervised segmentation method for pathology that leverages pre-trained deep learning models to generate segmentation masks without requiring additional training or finetuning. By factorizing spatial features extracted from these models into segmentation masks and concept features, F-SEG enables the use of recently developed pathology foundation models for semantic segmentation. The paper showcases the effectiveness of F-SEG by creating generic tissue phenotypes for H&E images through clustering models trained on features extracted from multiple deep learning models on The Cancer Genome Atlas Program (TCGA). This is followed by a demonstration of how clusters can be used to factorize corresponding segmentation masks using off-the-shelf deep learning models. Experimental results demonstrate that F-SEG provides robust unsupervised segmentation capabilities for H&E pathology images, with improved segmentation quality achieved through the utilization of pathology foundation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary F-SEG is a new way to segment cells in microscope images without needing lots of training data. It uses special AI models called “pathology foundation models” that are already good at understanding what’s going on in these types of images. F-SEG takes the features from these models and breaks them down into two parts: where things are in the image (like which cells are close together), and what those things are (like what type of cell it is). This helps F-SEG make more accurate segmentations. The researchers tested F-SEG on real microscope images and found that it works really well, especially when using these special pathology foundation models. |
Keywords
» Artificial intelligence » Clustering » Deep learning » Semantic segmentation » Unsupervised
