Summary of Multi-view and Multi-scale Alignment For Contrastive Language-image Pre-training in Mammography, by Yuexi Du et al.
Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in Mammography
by Yuexi Du, John Onofrey, Nicha C. Dvornek
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to adapting Contrastive Language-Image Pre-training (CLIP) models for medical image analysis in mammography. The current CLIP applications primarily focus on modalities like chest X-rays, leaving other important modalities under-explored due to data and computational limitations. To address these challenges, the authors develop a specialized supervision framework that leverages the multi-view nature of mammography, design a symmetric local alignment module for high-resolution images with small regions of interest, and incorporate a parameter-efficient fine-tuning approach for large language models pre-trained with medical knowledge. The proposed MaMA method outperforms state-of-the-art baselines on two large real-world mammography datasets (EMBED and RSNA-Mammo) with only 52% model size compared to the largest baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to use computers to help doctors analyze medical images, specifically for mammograms. Mammograms are important tests that help find breast cancer early. Right now, computer programs aren’t very good at helping doctors look at mammograms because they need lots of data and special equipment. The authors of this paper came up with a new way to make these computer programs better. They made a special program that can learn from small amounts of data and work well even when the pictures are really detailed. This is important because it could help doctors find breast cancer earlier, which would save lives. |
Keywords
* Artificial intelligence * Alignment * Fine tuning * Parameter efficient
