Summary of Unlocking the Power Of Spatial and Temporal Information in Medical Multimodal Pre-training, by Jinxia Yang et al.
Unlocking the Power of Spatial and Temporal Information in Medical Multimodal Pre-training
by Jinxia Yang, Bing Su, Wayne Xin Zhao, Ji-Rong Wen
First submitted to arxiv on: 30 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 Medical vision-language pre-training methods typically rely on the correspondence between paired medical images and radiological reports. However, existing methods have not fully utilized multi-view spatial images and temporal sequences of image-report pairs available in off-the-shelf datasets. This paper introduces Med-ST, a framework that fine-tunes spatial and temporal modeling to exploit information from multiple views of chest radiographs and historical records. For spatial modeling, Med-ST uses the Mixture of View Expert (MoVE) architecture to integrate different visual features from frontal and lateral views. The framework also establishes global alignment between whole images and texts and introduces modality-weighted local alignment between text tokens and image regions. For temporal modeling, Med-ST proposes a novel cross-modal bidirectional cycle consistency objective using forward mapping classification (FMC) and reverse mapping regression (RMR). By perceiving temporal information from simple to complex, Med-ST can learn temporal semantics. Experimental results across four tasks demonstrate the effectiveness of Med-ST, particularly in temporal classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medicines use special computers to help doctors better understand medical images. They usually match pictures with reports from doctors. But new data is available that includes many different views and records over time. This paper shows how to use this extra information to make the computers even better. It’s like using a map to find your way around a big hospital. The special computer, called Med-ST, uses two main parts: one for matching pictures with reports, and another for understanding how things change over time. It does this by looking at different parts of the picture and comparing them to words from the report. The results show that Med-ST can help doctors make better decisions. |
Keywords
» Artificial intelligence » Alignment » Classification » Regression » Semantics
