Clinically-Grounded Counterfactual Reasoning for Medical Video Diagnosis

• URL: http://arxiv.org/abs/2605.26483v1
• Date: Tue, 26 May 2026 02:46:15 GMT
• Title: Clinically-Grounded Counterfactual Reasoning for Medical Video Diagnosis
• Abstract summary: This work introduces MedVCR, a counterfactual reasoning framework that mimics clinical diagnostic thinking.<n> MedVCR comprises three components: a Counterfactual Generator that synthesizes tissue evolution under specified pathological states.<n>It is evaluated under both fully supervised (e.g., colposcopy) and weakly supervised (e.g., colonoscopy) video diagnosis settings, yielding 2.6%-10.2% performance gains.
• Score: 47.559337485629904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical video diagnosis involves inferring clinical decisions from dynamic tissue responses throughout examination processes. Existing methods rely on an end-to-end learning paradigm that i) focuses on appearance rather than pathology, ii) lacks clinical priors, and iii) reasons solely from observations without counterfactual comparison. This work introduces MedVCR, a counterfactual reasoning framework that mimics clinical diagnostic thinking. MedVCR comprises three components: a Counterfactual Generator that synthesizes tissue evolution under specified pathological states via a diffusion-based manner; a Counterfactual Representation Learning module that encodes diagnostic knowledge through clinical rules (i.e., temporal consistency, pathological separability, and counterfactual alignment); and a Dual Diagnostic Prediction strategy that integrates video-level assessment with frame-level counterfactual analysis. MedVCR is evaluated under both fully supervised (e.g., colposcopy) and weakly supervised (e.g., colonoscopy) video diagnosis settings, yielding 2.6%-10.2% performance gains compared with leading baselines. Comprehensive ablation studies further validate the effectiveness of each component. The code will be released.

Related papers

• Evaluating Multi-Turn Multimodal Diagnostic Reasoning on Challenging Real-World Clinical Cases [59.5328455571863]
  ClinMM-Bench is the largest multi-turn multimodal clinical diagnostic evaluation benchmark to date.<n>It contains 1,089 challenging real-world clinical cases and 3,760 medical images across eight specialties.<n> proprietary models achieved the highest overall diagnostic accuracy, but the proportion of completely correct diagnoses remained limited.
  arXiv  Detail & Related papers  (2026-07-28T16:19:03Z)
• Divide-then-Diagnose: Weaving Clinician-Inspired Contexts for Ultra-Long Capsule Endoscopy Videos [83.01543151239254]
  We define a new task, diagnosis-driven CE video summarization.<n>VideoCAP is the first CE dataset with diagnosis-driven annotations.<n>DiCE is a clinician-inspired framework that mirrors the standard CE reading workflow.
  arXiv  Detail & Related papers  (2026-04-23T16:07:51Z)
• 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)
• AgentsEval: Clinically Faithful Evaluation of Medical Imaging Reports via Multi-Agent Reasoning [73.50200033931148]
  We introduce AgentsEval, a multi-agent stream reasoning framework that emulates the collaborative diagnostic workflow of radiologists.<n>By dividing the evaluation process into interpretable steps including criteria definition, evidence extraction, alignment, and consistency scoring, AgentsEval provides explicit reasoning traces and structured clinical feedback.<n> Experimental results demonstrate that AgentsEval delivers clinically aligned, semantically faithful, and interpretable evaluations that remain robust under paraphrastic, semantic, and stylistic perturbations.
  arXiv  Detail & Related papers  (2026-01-23T11:59:13Z)
• MedKGI: Iterative Differential Diagnosis with Medical Knowledge Graphs and Information-Guided Inquiring [39.776843923694244]
  We propose MedKGI, a diagnostic framework grounded in clinical practices.<n>We show that MedKGI outperforms strong LLM baselines in both diagnostic accuracy and inquiry efficiency.
  arXiv  Detail & Related papers  (2025-12-30T12:31:53Z)
• ClinDEF: A Dynamic Evaluation Framework for Large Language Models in Clinical Reasoning [58.01333341218153]
  We propose ClinDEF, a dynamic framework for assessing clinical reasoning in LLMs through simulated diagnostic dialogues.<n>Our method generates patient cases and facilitates multi-turn interactions between an LLM-based doctor and an automated patient agent.<n>Experiments show that ClinDEF effectively exposes critical clinical reasoning gaps in state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2025-12-29T12:58:58Z)
• Simulating Viva Voce Examinations to Evaluate Clinical Reasoning in Large Language Models [51.91760712805404]
  We introduce VivaBench, a benchmark for evaluating sequential clinical reasoning in large language models (LLMs)<n>Our dataset consists of 1762 physician-curated clinical vignettes structured as interactive scenarios that simulate a (oral) examination in medical training.<n>Our analysis identified several failure modes that mirror common cognitive errors in clinical practice.
  arXiv  Detail & Related papers  (2025-10-11T16:24:35Z)
• Ensemble Deep Learning and LLM-Assisted Reporting for Automated Skin Lesion Diagnosis [2.9307254086347427]
  We introduce a unified framework that reimagines AI integration for dermatological diagnostics.<n>First, a purposefully heterogeneous ensemble of architecturally diverse convolutional neural networks provides complementary diagnostic perspectives.<n>Second, we embed large language model capabilities directly into the diagnostic workflow, transforming classification outputs into clinically meaningful assessments.
  arXiv  Detail & Related papers  (2025-10-05T08:07:33Z)
• OpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease Diagnosis [11.775648630734949]
  Alzheimer's disease (AD) cannot be reversed or cured, but timely diagnosis can significantly reduce the burden of treatment and care. Current research on AD diagnosis models usually regards the diagnosis task as a typical classification task. We propose OpenClinicalAI for direct AD diagnosis in complex and uncertain clinical settings.
  arXiv  Detail & Related papers  (2023-07-03T12:35:03Z)

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.