Summary of Paths: a Hierarchical Transformer For Efficient Whole Slide Image Analysis, by Zak Buzzard et al.
PATHS: A Hierarchical Transformer for Efficient Whole Slide Image Analysis
by Zak Buzzard, Konstantin Hemker, Nikola Simidjievski, Mateja Jamnik
First submitted to arxiv on: 27 Nov 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 proposes Pathology Transformer with Hierarchical Selection (PATHS), a novel method for weakly supervised representation learning in computational pathology. PATHS mimics the cross-magnification process used by human pathologists, recursively filtering patches at each magnification level to relevant regions. This approach overcomes the limitations of processing entire slides and enables quadratic self-attention and interpretable region importance measures. The authors apply PATHS to five datasets from The Cancer Genome Atlas (TCGA) and achieve superior performance on slide-level prediction tasks compared to previous methods, despite processing only a small proportion of the slide. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to analyze pictures of cancer tissue that helps doctors make better predictions. Normally, computers look at these images as a bunch of tiny pieces and try to understand what they mean. But this approach can be slow and noisy because most of those tiny pieces aren’t important for the diagnosis. The researchers created a new method called PATHS that works like how human pathologists examine slides under different magnifications. It looks at the bigger picture, picks out the relevant parts, and ignores the rest. This makes it faster and more accurate than previous methods. They tested this approach on many datasets and found that it did better than others at predicting things like cancer type or survival rate. |
Keywords
» Artificial intelligence » Representation learning » Self attention » Supervised » Transformer
