Thinking Like a Radiologist: A Dataset for Anatomy-Guided Interleaved Vision Language Reasoning in Chest X-ray Interpretation

• URL: http://arxiv.org/abs/2602.12843v1
• Date: Fri, 13 Feb 2026 11:49:32 GMT
• Title: Thinking Like a Radiologist: A Dataset for Anatomy-Guided Interleaved Vision Language Reasoning in Chest X-ray Interpretation
• Abstract summary: Radiological diagnosis is a perceptual process in which visual inspection and language reasoning are repeatedly interleaved.<n>We introduce MMRad-IVL-22K, the first large-scale dataset designed for natively interleaved visual language reasoning in chest X-ray interpretation.
• Score: 40.235535792762285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Radiological diagnosis is a perceptual process in which careful visual inspection and language reasoning are repeatedly interleaved. Most medical large vision language models (LVLMs) perform visual inspection only once and then rely on text-only chain-of-thought (CoT) reasoning, which operates purely in the linguistic space and is prone to hallucination. Recent methods attempt to mitigate this issue by introducing visually related coordinates, such as bounding boxes. However, these remain a pseudo-visual solution: coordinates are still text and fail to preserve rich visual details like texture and density. Motivated by the interleaved nature of radiological diagnosis, we introduce MMRad-IVL-22K, the first large-scale dataset designed for natively interleaved visual language reasoning in chest X-ray interpretation. MMRad-IVL-22K reflects a repeated cycle of reasoning and visual inspection workflow of radiologists, in which visual rationales complement textual descriptions and ground each step of the reasoning process. MMRad-IVL-22K comprises 21,994 diagnostic traces, enabling systematic scanning across 35 anatomical regions. Experimental results on advanced closed-source LVLMs demonstrate that report generation guided by multimodal CoT significantly outperforms that guided by text-only CoT in clinical accuracy and report quality (e.g., 6\% increase in the RadGraph metric), confirming that high-fidelity interleaved vision language evidence is a non-substitutable component of reliable medical AI. Furthermore, benchmarking across seven state-of-the-art open-source LVLMs demonstrates that models fine-tuned on MMRad-IVL-22K achieve superior reasoning consistency and report quality compared with both general-purpose and medical-specific LVLMs. The project page is available at https://github.com/qiuzyc/thinking_like_a_radiologist.

Related papers

• Seeing Through Experts Eyes A Foundational Vision Language Model Trained on Radiologists Gaze and Reasoning [13.62559208250024]
  We introduce GazeX, a vision language model that leverages radiologists' eye tracking data as a behavioral prior to model expert diagnostic reasoning.<n>We demonstrate that GazeX produces more accurate, interpretable, and expert consistent outputs across radiology report generation, disease grounding, and visual question answering.
  arXiv  Detail & Related papers  (2026-04-15T18:19:05Z)
• Visual Alignment of Medical Vision-Language Models for Grounded Radiology Report Generation [25.148217482604746]
  We propose VALOR:Visual Alignment of Medical Vision-Language Models for Radiology Report Generation.<n>Our method introduces a reinforcement learning-based post-alignment framework utilizing Group-Relative Proximal Optimization (GRPO)<n>Experiments on multiple benchmarks demonstrate that VALOR substantially improves factual accuracy and visual grounding, achieving significant performance gains over state-of-the-art report generation methods.
  arXiv  Detail & Related papers  (2025-12-18T05:48:21Z)
• S-Chain: Structured Visual Chain-of-Thought For Medicine [81.97605645734741]
  We introduce S-Chain, the first large-scale dataset of 12,000 expert-annotated medical images with bounding boxes and structured visual CoT (SV-CoT)<n>The dataset further supports 16 languages, totaling over 700k VQA pairs for broad multilingual applicability.<n>S-Chain establishes a new benchmark for grounded medical reasoning and paves the way toward more trustworthy and explainable medical vision-language models.
  arXiv  Detail & Related papers  (2025-10-26T15:57:14Z)
• XBench: A Comprehensive Benchmark for Visual-Language Explanations in Chest Radiography [6.447908430647854]
  We present the first systematic benchmark for evaluating cross-modal interpretability in chest X-rays.<n>We generate visual explanations using cross-attention and similarity-based localization maps.<n>We quantitatively assess their alignment with radiologist-annotated regions across multiple pathologies.
  arXiv  Detail & Related papers  (2025-10-22T13:52:19Z)
• X-Ray-CoT: Interpretable Chest X-ray Diagnosis with Vision-Language Models via Chain-of-Thought Reasoning [0.0]
  We propose X-Ray-CoT (Chest X-Ray Chain-of-Thought), a novel framework for intelligent chest X-ray diagnosis and interpretable report generation.<n>X-Ray-CoT simulates human radiologists' "chain-of-thought" by first extracting multi-modal features and visual concepts.<n>It achieves competitive quantitative performance, with a Balanced Accuracy of 80.52% and F1 score of 78.65% for disease diagnosis.
  arXiv  Detail & Related papers  (2025-08-17T18:00:41Z)
• RadAlign: Advancing Radiology Report Generation with Vision-Language Concept Alignment [10.67889367763112]
  RadAlign is a novel framework that combines the predictive accuracy of vision-language models with the reasoning capabilities of large language models.<n>Our framework maintains strong clinical interpretability while reducing hallucinations, advancing automated medical imaging and report analysis through integrated predictive and generative AI.
  arXiv  Detail & Related papers  (2025-01-13T17:55:32Z)
• ViKL: A Mammography Interpretation Framework via Multimodal Aggregation of Visual-knowledge-linguistic Features [54.37042005469384]
  We announce MVKL, the first multimodal mammography dataset encompassing multi-view images, detailed manifestations and reports. Based on this dataset, we focus on the challanging task of unsupervised pretraining. We propose ViKL, a framework that synergizes Visual, Knowledge, and Linguistic features.
  arXiv  Detail & Related papers  (2024-09-24T05:01:23Z)
• Uncertainty-aware Medical Diagnostic Phrase Identification and Grounding [72.18719355481052]
  We introduce a novel task called Medical Report Grounding (MRG)<n>MRG aims to directly identify diagnostic phrases and their corresponding grounding boxes from medical reports in an end-to-end manner.<n>We propose uMedGround, a robust and reliable framework that leverages a multimodal large language model to predict diagnostic phrases.
  arXiv  Detail & Related papers  (2024-04-10T07:41:35Z)
• Intensive Vision-guided Network for Radiology Report Generation [22.030289124516326]
  We propose a Globally-intensive Attention (GIA) module in the medical image encoder to simulate and integrate multi-view vision perception. We also explore how to involve multi-modal signals to generate precisely matched reports, i.e., how to integrate previously predicted words with region-aware visual content in next word prediction.
  arXiv  Detail & Related papers  (2024-02-06T06:46:46Z)
• XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models [72.8965643836841]
  We introduce XrayGPT, a novel conversational medical vision-language model.<n>It can analyze and answer open-ended questions about chest radiographs.<n>We generate 217k interactive and high-quality summaries from free-text radiology reports.
  arXiv  Detail & Related papers  (2023-06-13T17:59:59Z)
• Cross-Modal Causal Intervention for Medical Report Generation [107.76649943399168]
  Radiology Report Generation (RRG) is essential for computer-aided diagnosis and medication guidance.<n> generating accurate lesion descriptions remains challenging due to spurious correlations from visual-linguistic biases.<n>We propose a two-stage framework named CrossModal Causal Representation Learning (CMCRL)<n> Experiments on IU-Xray and MIMIC-CXR show that our CMCRL pipeline significantly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-16T07:23:55Z)
• Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report Generation [107.3538598876467]
  We propose an Auxiliary Signal-Guided Knowledge-Decoder (ASGK) to mimic radiologists' working patterns. ASGK integrates internal visual feature fusion and external medical linguistic information to guide medical knowledge transfer and learning.
  arXiv  Detail & Related papers  (2020-06-06T01:00:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.