Summary of Facmic: Federated Adaptative Clip Model For Medical Image Classification, by Yihang Wu et al.
FACMIC: Federated Adaptative CLIP Model for Medical Image Classification
by Yihang Wu, Christian Desrosiers, Ahmad Chaddad
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 a federated adaptive Contrastive Language Image Pretraining (CLIP) model designed for classification tasks in medical image analysis. The CLIP model incorporates a light-weight and efficient feature attention module to select suitable features for each client’s data, while also proposing a domain adaptation technique to reduce differences in data distribution between clients. Experimental results on four publicly available datasets demonstrate the superior performance of FACMIC in dealing with real-world and multisource medical imaging data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors analyze medical images without sharing their personal information. It creates a special model that can learn from lots of different medical image sources, even if they’re not all alike. The model is good at picking out the most important features to help it make accurate diagnoses. This could be really helpful for people who have rare diseases or need a second opinion. |
Keywords
» Artificial intelligence » Attention » Classification » Domain adaptation » Pretraining
