Summary of See Detail Say Clear: Towards Brain Ct Report Generation Via Pathological Clue-driven Representation Learning, by Chengxin Zheng et al.
See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning
by Chengxin Zheng, Junzhong Ji, Yanzhao Shi, Xiaodan Zhang, Liangqiong Qu
First submitted to arxiv on: 29 Sep 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 This paper proposes a novel approach to generating brain CT reports that can aid physicians in diagnosing cranial diseases. The authors identify two key challenges: redundant visual representations and shifted semantic representations, which hinder the coherence of report generation. To address these issues, they introduce the Pathological Clue-driven Representation Learning (PCRL) model, which builds cross-modal representations based on pathological clues. These clues are constructed from segmented regions, pathological entities, and report themes to grasp visual pathological patterns. The authors also use a unified large language model (LLM) with task-tailored instructions to bridge the gap between representation learning and report generation. Experimental results show that their method outperforms previous methods and achieves state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors make better diagnoses by creating accurate brain CT reports. Right now, there are two big problems: too much extra information in the scans and limited medical training data. To solve these issues, the researchers created a new model called PCRL that uses clues from different parts of the scan to learn how to represent visual patterns. They also used a special language model with instructions to help it generate reports that are accurate and helpful. The results show that their method is the best so far. |
Keywords
» Artificial intelligence » Language model » Large language model » Representation learning
