[2511.10065] RadHiera: Semantic Hierarchical Reinforcement Learning for Medical Report Generation

Computer Science > Artificial Intelligence arXiv:2511.10065 (cs) [Submitted on 13 Nov 2025 (v1), last revised 23 Mar 2026 (this version, v2)] Title:RadHiera: Semantic Hierarchical Reinforcement Learning for Medical Report Generation Authors:Bodong Du, Honglong Yang, Xiaomeng Li View a PDF of the paper titled RadHiera: Semantic Hierarchical Reinforcement Learning for Medical Report Generation, by Bodong Du and 2 other authors View PDF HTML (experimental) Abstract:Vision-language models have shown promising results in radiology report generation. However, most existing methods generate reports as flat text and do not explicitly model the semantic dependency between the Findings and Impression sections, which can lead to inconsistencies between clinical observations and diagnostic conclusions. In this paper, we propose RadHiera, a semantic hierarchical reinforcement learning framework for radiology report generation. RadHiera follows the semantic organization of radiology reports by first optimizing overall report quality, then improving the diagnostic accuracy of the Impression section, and finally enforcing consistency between Findings and Impression so that diagnostic conclusions are supported by clinical evidence. Specifically, we begin with a base reward that combines linguistic quality and medical factuality to provide supervision on the whole report. On this basis, we introduce a severity-aware reward for the Impression section that places greater emphasis on errors in...