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🧠 AI⚪ NeutralImportance 4/10
Exploiting Low-Dimensional Manifold of Features for Few-Shot Whole Slide Image Classification
arXiv – CS AI|Conghao Xiong, Zhengrui Guo, Zhe Xu, Yifei Zhang, Raymond Kai-Yu Tong, Si Yong Yeo, Hao Chen, Joseph J. Y. Sung, Irwin King||3 views
🤖AI Summary
Researchers propose a Manifold Residual (MR) block to address overfitting in few-shot Whole Slide Image classification by preserving the low-dimensional manifold geometry of pathology foundation model features. The geometry-aware approach achieves state-of-the-art results with fewer parameters by using a fixed random matrix as geometric anchor and a trainable low-rank residual pathway.
Key Takeaways
- →Few-shot WSI classification overfitting is identified as a geometric problem rather than just data scarcity.
- →Pathology foundation model features exhibit low-dimensional manifold geometry that gets distorted by linear layers.
- →The MR block uses a dual pathway approach with fixed geometric anchoring and trainable residual learning.
- →The method achieves state-of-the-art performance with significantly fewer parameters than existing approaches.
- →The solution provides a plug-and-play module that can be integrated into existing multiple instance learning models.
#machine-learning#medical-ai#few-shot-learning#computer-vision#pathology#manifold-learning#foundation-models
Read Original →via arXiv – CS AI
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