Summary of Uni-mlip: Unified Self-supervision For Medical Vision Language Pre-training, by Ameera Bawazir et al.
Uni-Mlip: Unified Self-supervision for Medical Vision Language Pre-training
by Ameera Bawazir, Kebin Wu, Wenbin Li
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 A recent surge in vision-language pre-training via contrastive learning has led to significant performance gains across computer vision tasks. However, in the medical domain, obtaining multimodal data is often challenging due to privacy, sensitivity, and annotation complexity. To address this issue, we propose Uni-Mlip, a unified self-supervision framework designed to enhance medical vision-language pre-training. Uni-Mlip integrates cross-modality, uni-modality, and fused-modality self-supervision techniques at the data-level and feature-level. Our approach also incorporates tailored uni-modal image self-supervision for medical images. Experimental results across various datasets demonstrate that Uni-Mlip outperforms current state-of-the-art methods in image-text retrieval, image classification, and visual question answering (VQA). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to train a computer model to analyze medical images. This is tricky because it’s hard to get the data you need, especially if it’s sensitive or private. Researchers have found that by using special techniques called self-supervision, they can improve their models’ performance without needing as much data. They’ve created a new framework called Uni-Mlip that combines different types of self-supervision to help medical image analysis models learn better. The results are impressive – Uni-Mlip outperforms other methods in several important tasks. |
Keywords
» Artificial intelligence » Image classification » Question answering
