Summary of Personalize to Generalize: Towards a Universal Medical Multi-modality Generalization Through Personalization, by Zhaorui Tan et al.
Personalize to generalize: Towards a universal medical multi-modality generalization through personalization
by Zhaorui Tan, Xi Yang, Tan Pan, Tianyi Liu, Chen Jiang, Xin Guo, Qiufeng Wang, Anh Nguyen, Yuan Qi, Kaizhu Huang, Yuan Cheng
First submitted to arxiv on: 9 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 proposed approach to multi-modal generalization in medical imaging tasks addresses significant challenges posed by modality gaps and individual variations. The current state of affairs neglects these differences, focusing solely on common anatomical features, which can lead to weakened generalization. To overcome this limitation, the authors introduce an innovative method that approximates personalized invariant representations across various modalities through learnable biological priors and individual-level constraints. This approach is validated through extensive experimental results, demonstrating improved performance and generalization across diverse scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make medical imaging models better at working with different types of scans. Right now, these models struggle because they don’t account for differences between individual people’s bodies. To solve this problem, the researchers suggest creating personalized representations that take into consideration things like organ size and metabolic rate. This approach is tested on various medical tasks and shows significant improvements in performance. |
Keywords
» Artificial intelligence » Generalization » Multi modal
