Summary of Mvketr: Chest Ct Report Generation with Multi-view Perception and Knowledge Enhancement, by Xiwei Deng et al.
MvKeTR: Chest CT Report Generation with Multi-View Perception and Knowledge Enhancement
by Xiwei Deng, Xianchun He, Jiangfeng Bao, Yudan Zhou, Shuhui Cai, Congbo Cai, Zhong Chen
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed Multi-view perception Knowledge-enhanced Transformer (MvKeTR) aims to mimic the diagnostic workflow of clinicians by effectively synthesizing diagnostic information from multiple anatomical views and incorporating domain knowledge into the diagnosis procedure. This novel approach addresses limitations in existing works, which fail to incorporate diagnostic information from multiple views and lack clinical expertise essential for accurate diagnosis. The MvKeTR model consists of a Multi-View Perception Aggregator (MVPA) that utilizes view-aware attention to synthesize diagnostic information and a Cross-Modal Knowledge Enhancer (CMKE) that retrieves relevant reports based on the query volume. KANs with learnable nonlinear activation functions are employed as building blocks for both modules, allowing them to capture intricate diagnostic patterns in CT interpretation. The method outperforms prior state-of-the-art models across almost all metrics on the public CTRG-Chest-548K dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to generate medical reports that helps doctors make better diagnoses. It uses special computers that can look at different views of patient scans and combine them with information from past reports to get more accurate results. The system is called MvKeTR, which stands for Multi-view perception Knowledge-enhanced Transformer. It has two main parts: one that combines the different scan views and another that finds similar past reports to use as a guide. The method uses special computer programs called KANs that can learn from data and get better at recognizing patterns in medical images. This approach was tested on a large dataset of chest scans and showed better results than other methods. |
Keywords
» Artificial intelligence » Attention » Transformer
