See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning

• URL: http://arxiv.org/abs/2409.19676v2
• Date: Tue, 1 Oct 2024 10:42:32 GMT
• Title: See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning
• Authors: Chengxin Zheng, Junzhong Ji, Yanzhao Shi, Xiaodan Zhang, Liangqiong Qu,
• Abstract summary: We introduce a Pathological Clue-driven Representation Learning (PCRL) model to build cross-modal representations based on pathological clues. Specifically, we construct pathological clues from perspectives of segmented regions, pathological entities, and report themes. To adapt the representations for the text generation task, we bridge the gap between representation learning and report generation by using a unified large language model (LLM) with task-tailored instructions.
• Score: 12.40415847810958
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Brain CT report generation is significant to aid physicians in diagnosing cranial diseases. Recent studies concentrate on handling the consistency between visual and textual pathological features to improve the coherence of report. However, there exist some challenges: 1) Redundant visual representing: Massive irrelevant areas in 3D scans distract models from representing salient visual contexts. 2) Shifted semantic representing: Limited medical corpus causes difficulties for models to transfer the learned textual representations to generative layers. This study introduces a Pathological Clue-driven Representation Learning (PCRL) model to build cross-modal representations based on pathological clues and naturally adapt them for accurate report generation. Specifically, we construct pathological clues from perspectives of segmented regions, pathological entities, and report themes, to fully grasp visual pathological patterns and learn cross-modal feature representations. To adapt the representations for the text generation task, we bridge the gap between representation learning and report generation by using a unified large language model (LLM) with task-tailored instructions. These crafted instructions enable the LLM to be flexibly fine-tuned across tasks and smoothly transfer the semantic representation for report generation. Experiments demonstrate that our method outperforms previous methods and achieves SoTA performance. Our code is available at "https://github.com/Chauncey-Jheng/PCRL-MRG".

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