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About this paper
Kevin Thandiackal, Boqi Chen, Pushpak Pati, Guillaume Jaume, Drew F. K. Williamson, Maria Gabrani, Orcun Goksel
ZOOMMIL is a multi-scale Multiple Instance Learning framework that uses differentiable zooming to hierarchically identify and process informative regions within gigapixel Whole-Slide Images. It significantly reduces computational requirements by up to 40 times while maintaining or improving classification performance across various cancer detection and subtyping tasks.
Key insights
- Mimicking a pathologist's hierarchical examination strategy (identifying ROIs at low magnification and zooming in) is highly effective for improving WSI analysis efficiency and performance.
- An end-to-end learnable and differentiable mechanism for patch selection (differentiable Top-K) is crucial for optimizing the zooming process effectively.
- Decoupling the attention used for WSI representation from the attention used to guide patch selection for zooming provides complementary information, enhancing overall model performance.
Summary by alphaXiv.
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