Summary of Random Token Fusion For Multi-view Medical Diagnosis, by Jingyu Guo and Christos Matsoukas and Fredrik Strand and Kevin Smith
Random Token Fusion for Multi-View Medical Diagnosis
by Jingyu Guo, Christos Matsoukas, Fredrik Strand, Kevin Smith
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces Random Token Fusion (RTF), a novel technique to enhance multi-view medical image analysis using vision transformers. Existing approaches are prone to overfitting and rely on view-specific features, which can lead to trivial solutions. RTF integrates randomness into the feature fusion process during training, addressing overfitting and enhancing robustness and accuracy without additional cost at inference. The approach is validated on standard mammography and chest X-ray benchmark datasets, demonstrating consistent performance improvements over existing fusion methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Random Token Fusion (RTF) helps doctors make better diagnoses from medical images. Medical imaging machines take pictures from different angles, but current computer programs are not good at combining these views to improve diagnosis accuracy. RTF is a new way to analyze these images using special computer models called vision transformers. By adding some randomness to the analysis process, RTF makes sure the program doesn’t get too good at one specific view and stops working well with other views. This means doctors can trust the diagnoses from RTF-based programs more. |
Keywords
» Artificial intelligence » Inference » Overfitting » Token
