NeuroSymb-MRG: Differentiable Abductive Reasoning with Active Uncertainty Minimization for Radiology Report Generation
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910102132359168 |
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| author | Fu, Rong Lyu, Yiqing Meng, Chunlei Qi, Muge Jin, Yabin Zhao, Qi Bao, Li Gao, Juntao Shi, Fuqian Dey, Nilanjan Luo, Wei Fong, Simon |
| author_facet | Fu, Rong Lyu, Yiqing Meng, Chunlei Qi, Muge Jin, Yabin Zhao, Qi Bao, Li Gao, Juntao Shi, Fuqian Dey, Nilanjan Luo, Wei Fong, Simon |
| contents | Automatic generation of radiology reports seeks to reduce clinician workload while improving documentation consistency. Existing methods that adopt encoder-decoder or retrieval-augmented pipelines achieve progress in fluency but remain vulnerable to visual-linguistic biases, factual inconsistency, and lack of explicit multi-hop clinical reasoning. We present NeuroSymb-MRG, a unified framework that integrates NeuroSymbolic abductive reasoning with active uncertainty minimization to produce structured, clinically grounded reports. The system maps image features to probabilistic clinical concepts, composes differentiable logic-based reasoning chains, decodes those chains into templated clauses, and refines the textual output via retrieval and constrained language-model editing. An active sampling loop driven by rule-level uncertainty and diversity guides clinician-in-the-loop adjudication and promptbook refinement. Experiments on standard benchmarks demonstrate consistent improvements in factual consistency and standard language metrics compared to representative baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01756 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NeuroSymb-MRG: Differentiable Abductive Reasoning with Active Uncertainty Minimization for Radiology Report Generation Fu, Rong Lyu, Yiqing Meng, Chunlei Qi, Muge Jin, Yabin Zhao, Qi Bao, Li Gao, Juntao Shi, Fuqian Dey, Nilanjan Luo, Wei Fong, Simon Computer Vision and Pattern Recognition Automatic generation of radiology reports seeks to reduce clinician workload while improving documentation consistency. Existing methods that adopt encoder-decoder or retrieval-augmented pipelines achieve progress in fluency but remain vulnerable to visual-linguistic biases, factual inconsistency, and lack of explicit multi-hop clinical reasoning. We present NeuroSymb-MRG, a unified framework that integrates NeuroSymbolic abductive reasoning with active uncertainty minimization to produce structured, clinically grounded reports. The system maps image features to probabilistic clinical concepts, composes differentiable logic-based reasoning chains, decodes those chains into templated clauses, and refines the textual output via retrieval and constrained language-model editing. An active sampling loop driven by rule-level uncertainty and diversity guides clinician-in-the-loop adjudication and promptbook refinement. Experiments on standard benchmarks demonstrate consistent improvements in factual consistency and standard language metrics compared to representative baselines. |
| title | NeuroSymb-MRG: Differentiable Abductive Reasoning with Active Uncertainty Minimization for Radiology Report Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.01756 |