Look-Closer-Then-Diagnose: Confidence-Aware Ultrasound VQA via Active Zooming

• URL: http://arxiv.org/abs/2605.21652v2
• Date: Sat, 23 May 2026 13:26:54 GMT
• Title: Look-Closer-Then-Diagnose: Confidence-Aware Ultrasound VQA via Active Zooming
• Abstract summary: Vision-Language Models (VLMs) have significantly advanced medical visual question answering, yet their performance in ultrasound remains suboptimal.<n>We propose a framework specifically designed to consider the sonographer's cognitive workflow.<n>We first introduce a structured Zoom-then-Diagnose paradigm, which replicates the interactive search process to enable lesion-focused reasoning.
• Score: 41.40761036883243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Models (VLMs) have significantly advanced medical visual question answering, yet their performance in ultrasound remains suboptimal. In clinical practice, sonographers explicitly focus on lesion regions to formulate reports, though diagnostic interpretations sometimes vary due to inherent subjectivity. However, existing VLMs are not explicitly structured to interactively zoom into lesions prior to diagnosis; moreover, they typically treat annotations as unbiased ground truths, failing to account for their inherent subjectivity and ambiguity. In this paper, we propose a framework specifically designed to consider the sonographer's cognitive workflow. We first introduce a structured Zoom-then-Diagnose paradigm, which replicates the interactive search process to enable lesion-focused reasoning. Furthermore, within the Group Relative Policy Optimization (GRPO) framework, we introduce an uncertainty-aware reward derived from stochastic group-wise rollouts to estimate prediction consistency as a proxy for model confidence. Together, these two components encourage the model to reinforce accurate predictions on clear cases while remaining cautious under ambiguity. Experiments across liver, breast, and thyroid datasets show that our framework improves lesion localization by 39.3\%, demonstrating that our model has learned the ability to actively look closer and diagnose.

Related papers

• Analyzing and Improving Fine-grained Preference Optimization in Medical LVLMs [53.697403481143404]
  Large Vision-Language Models (LVLMs) have achieved strong performance across medical imaging tasks.<n>But they remain prone to factual inconsistencies, poor visual grounding, and misalignment with clinically meaningful feedback.
  arXiv  Detail & Related papers  (2026-06-10T18:35:36Z)
• Medical Context Distorts Decisions in Clinical Vision Language Models [7.537887356414114]
  Vision-language models (VLMs) are increasingly proposed for clinical decision support, yet their reliability in real-world scenarios remains poorly characterized.<n>This paper identifies three failure modes: modality over-reliance on text over images, (2) spurious reliance on irrelevant clinical history, and (3) prompt sensitivity across semantically equivalent inputs.
  arXiv  Detail & Related papers  (2026-05-17T13:11:38Z)
• Dialectic-Med: Mitigating Diagnostic Hallucinations via Counterfactual Adversarial Multi-Agent Debate [2.645243404278707]
  We propose Dialectic-Med, a multi-agent framework that enforces diagnostic rigor through adversarial dialectics.<n>By explicitly modeling the cognitive process of falsification, our framework guarantees that diagnostic reasoning is tightly grounded in verified visual regions.
  arXiv  Detail & Related papers  (2026-04-13T10:09:36Z)
• Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models [56.29123284262618]
  This work proposes leveraging the universal representation capabilities of visual foundation models to estimate inherent data uncertainty.<n>We analyze the feature diversity of the model's decoded representations and quantify their singular value energy to define the semantic perception scale for each class.<n>Based on this foundation, we design two uncertainty-driven application strategies: (1) the aleatoric uncertainty-aware data filtering mechanism to eliminate potentially noisy samples and enhance model learning quality; and (2) the dynamic uncertainty-aware optimization strategy that adaptively adjusts class-specific loss weights during training based on the semantic perception scale, combined with a label denoising mechanism to improve training stability
  arXiv  Detail & Related papers  (2026-04-13T03:59:54Z)
• Clinical Cognition Alignment for Gastrointestinal Diagnosis with Multimodal LLMs [63.535652574541764]
  Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in medical image analysis.<n>Their application in gastrointestinal endoscopy is currently hindered by two critical limitations.<n>We propose a novel Clinical-Cognitive-Aligned (CogAlign) framework to address these challenges.
  arXiv  Detail & Related papers  (2026-03-21T07:47:37Z)
• Deep Expert Injection for Anchoring Retinal VLMs with Domain-Specific Knowledge [66.67024684187915]
  Large Vision Language Models (LVLMs) show immense potential for automated ophthalmic diagnosis.<n>Their clinical deployment is severely hindered by lacking domain-specific knowledge.<n>EyExIn is a framework designed to anchor retinal VLMs with expert knowledge via a Deep Expert Injection mechanism.
  arXiv  Detail & Related papers  (2026-03-07T09:43:49Z)
• Toward Guarantees for Clinical Reasoning in Vision Language Models via Formal Verification [12.60121003165514]
  Vision-language models (VLMs) show promise in drafting radiology reports, yet they frequently suffer from logical inconsistencies.<n>Standard lexical metrics heavily penalize clinical paraphrasing and fail to capture these deductive failures.<n>We introduce a neurosymbolic verification framework that deterministically audits the internal consistency of VLM-generated reports.
  arXiv  Detail & Related papers  (2026-02-27T15:49:59Z)
• Reasoning Visual Language Model for Chest X-Ray Analysis [30.318629424154206]
  We present a framework that brings chain-of-thought (CoT) reasoning to chest X-ray interpretation.<n>Inspired by reasoning-first training paradigms, our approach is designed to learn how experts reason, not just what they conclude.<n>We release code and the model NV-Reason-CXR-3B to support community progress toward trustworthy, explainable AI in chest radiography.
  arXiv  Detail & Related papers  (2025-10-28T00:48:00Z)
• Self-Supervised Anatomical Consistency Learning for Vision-Grounded Medical Report Generation [61.350584471060756]
  Vision-grounded medical report generation aims to produce clinically accurate descriptions of medical images.<n>We propose Self-Supervised Anatomical Consistency Learning (SS-ACL) to align generated reports with corresponding anatomical regions.<n>SS-ACL constructs a hierarchical anatomical graph inspired by the invariant top-down inclusion structure of human anatomy.
  arXiv  Detail & Related papers  (2025-09-30T08:59:06Z)
• Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models [52.2001050216955]
  Existing methods aim to enhance the performance of Medical Vision Language Model (MedVLM) by adjusting model structure, fine-tuning with high-quality data, or through preference fine-tuning.<n>We propose an expert-in-the-loop framework named Expert-Controlled-Free Guidance (Expert-CFG) to align MedVLM with clinical expertise without additional training.
  arXiv  Detail & Related papers  (2025-07-12T09:03:30Z)
• Unveiling Interpretability in Self-Supervised Speech Representations for Parkinson's Diagnosis [9.91077163490596]
  We propose a novel, interpretable framework specifically designed to support Parkinson's Disease diagnosis.<n>Through the design of simple yet effective cross-attention mechanisms, the proposed framework offers interpretability from two distinct but complementary perspectives.<n>Our method results competitive with state-of-the-art approaches, while also demonstrating robustness in cross-lingual scenarios.
  arXiv  Detail & Related papers  (2024-12-02T22:23:43Z)

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.