mlsanityData to 7 Oct 2026

DECIS: Dual-Evidence Corrective Verification for Interpretable Strabismus Diagnostic Decision-Making

arXiv 2606.09249 ↗First posted 8 Jun 2026cs.CVcited by 0

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Abstract (arXiv)

Strabismus is a common ocular disorder that requires fine-grained subtype diagnosis for individualized treatment planning. However, existing deep learning methods mainly provide diagnostic predictions without transparent reasoning, while recent large vision-language models (LVLMs), although promising for joint image understanding and report generation, remain highly prone to hallucination in this evidence-sensitive and rule-driven medical task. To address these challenges, we propose DECIS, a Dual-Evidence Corrective verification for Interpretable Strabismus diagnostic decision-making framework. DECIS transforms black-box end-to-end generation into a structured diagnostic process consisting of candidate hypothesis generation, dual-evidence constrained context, evidence-based corrective verification, and report generation. Specifically, we introduce a Dual-Evidence Constrained Context (DECC) mechanism that jointly organizes visual evidence from the photograph of the nine cardinal positions of gaze and evidence-based clinical diagnostic rules into a constrained context for reliable diagnostic reasoning. We further develop an Evidence-Based Corrective Verification (EBCV) mechanism that verifies whether the current diagnostic hypothesis is supported by visual evidence, heatmap-based visual cues, and evidence-based clinical diagnostic rules. Hypothesis refinement is triggered when inconsistency is detected. Experiments on a fine-grained strabismus benchmark demonstrate that DECIS not only outperforms other state-of-the-art diagnostic systems, improving the weighted F1 score from 72.0% to 91.3%, but also improves the clinical reliability (consistency, alignment, and completeness) of generated diagnostic reports. These results demonstrate that DECIS provides an effective solution for building accurate, evidence-based, and clinically interpretable strabismus diagnosis systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 37 of its 61 references that are in the map. First posted on arXiv 8 Jun 2026.

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References · 37

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2024The Llama 3 Herd of Modelscited by 14,192
  5. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  6. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  7. 2016Densely Connected Convolutional Networkscited by 6,691
  8. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  9. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  10. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  11. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  12. 2025Qwen3-VL Technical Reportcited by 2,534
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  1. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  2. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  3. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  4. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,051
  5. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
  6. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  7. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 724
  8. 2023Aligning Large Multimodal Models with Factually Augmented RLHFcited by 703
  9. 2023Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decodingcited by 671
  10. 2023RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedbackcited by 404
  11. 2024Hallucination of Multimodal Large Language Models: A Surveycited by 380
  12. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 317
  13. 2022Large Language Models are Better Reasoners with Self-Verificationcited by 304
  14. 2024Scaling Large Language Model-based Multi-Agent Collaborationcited by 235
  15. 2024Multi-Modal Hallucination Control by Visual Information Groundingcited by 196
  16. 2025HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptationcited by 112
  17. 2024Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluationcited by 79
  18. 2024Large Language Model with Region-guided Referring and Grounding for CT Report Generationcited by 27
  19. 2024CoMT: Chain-of-Medical-Thought Reduces Hallucination in Medical Report Generationcited by 25
  20. 2025Multiple LLM Agents Debate for Equitable Cultural Alignmentcited by 20
  21. 2024Ultrasound Report Generation with Cross-Modality Feature Alignment via Unsupervised Guidancecited by 16
  22. 2024Disease-informed Adaptation of Vision-Language Modelscited by 7
  23. 2025A Chain of Diagnosis Framework for Accurate and Explainable Radiology Report Generationcited by 6
  24. 2024Topicwise Separable Sentence Retrieval for Medical Report Generationcited by 5
  25. 2025AutoMedic: An Automated Evaluation Framework for Clinical Conversational Agents with Medical Dataset Groundingcited by 3

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