Summary of Clinical Domain Knowledge-derived Template Improves Post Hoc Ai Explanations in Pneumothorax Classification, by Han Yuan et al.
Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax Classification
by Han Yuan, Chuan Hong, Pengtao Jiang, Gangming Zhao, Nguyen Tuan Anh Tran, Xinxing Xu, Yet Yen Yan, Nan Liu
First submitted to arxiv on: 26 Mar 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 The proposed template-guided approach aims to improve the quality of explainable artificial intelligence (XAI) methods in pneumothorax diagnoses by incorporating clinical knowledge into model explanations. This is achieved by generating a template representing potential areas of pneumothorax occurrence and superimposing it on model explanations to filter out extraneous information. The approach is validated through comparative analysis of three XAI methods with and without the template guidance, demonstrating significant improvements in Intersection over Union (IoU) and Dice Similarity Coefficient (DSC) metrics when comparing model explanations and ground-truth lesion areas. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make artificial intelligence (AI) more understandable is being developed. The goal is to help doctors diagnose a serious condition called pneumothorax by making AI’s decisions more clear. To do this, the researchers created a special template that shows where pneumothorax might occur in a patient’s chest. This template is then used to improve how AI explains its diagnoses. The results show that using this template makes AI’s explanations much better and more accurate. |
