Summary of Multi-modal Vision Pre-training For Medical Image Analysis, by Shaohao Rui et al.
Multi-modal Vision Pre-training for Medical Image Analysis
by Shaohao Rui, Lingzhi Chen, Zhenyu Tang, Lilong Wang, Mianxin Liu, Shaoting Zhang, Xiaosong Wang
First submitted to arxiv on: 14 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 In this paper, researchers aim to improve medical image analysis by developing a self-supervised learning method that can learn cross-modal representations from multi-modal brain MRI scans. The proposed approach utilizes three proxy tasks: cross-modal image reconstruction, modality-aware contrastive learning, and modality template distillation. These tasks are designed to facilitate the learning of correlations between different imaging modalities, which is essential for real-world applications. The model is pre-trained on a large dataset containing over 2.4 million images from 16,022 scans of 3,755 patients. To evaluate the generalizability of the proposed method, extensive experiments are conducted on various benchmarks with ten downstream tasks. The results show that the proposed approach outperforms state-of-the-art pre-training methods, achieving improved Dice Score and accuracy in several segmentation and image classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to learn from medical images without needing labeled data. It’s like learning to recognize different shapes by looking at many pictures of animals, cars, and buildings. The researchers use three different “tasks” or challenges to help the computer learn how to combine information from different types of images. They test their method on a huge dataset of MRI scans and show that it works better than other approaches for several tasks. |
Keywords
» Artificial intelligence » Distillation » Image classification » Multi modal » Self supervised
