Prompting Medical Large Vision-Language Models to Diagnose Pathologies by Visual Question Answering

• URL: http://arxiv.org/abs/2407.21368v2
• Date: Sat, 01 Mar 2025 06:14:00 GMT
• Title: Prompting Medical Large Vision-Language Models to Diagnose Pathologies by Visual Question Answering
• Authors: Danfeng Guo, Demetri Terzopoulos,
• Abstract summary: We propose two prompting strategies for MLVLMs that reduce hallucination and improve VQA performance.<n> tested on the MIMIC-CXR-JPG and Chexpert datasets, our methods significantly improve the diagnostic F1 score.<n>Based on POPE metrics, it effectively suppresses the false negative predictions of existing LVLMs and improves Recall by approximately 0.07.
• Score: 6.087954428369633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Vision-Language Models (LVLMs) have achieved significant success in recent years, and they have been extended to the medical domain. Although demonstrating satisfactory performance on medical Visual Question Answering (VQA) tasks, Medical LVLMs (MLVLMs) suffer from the hallucination problem, which makes them fail to diagnose complex pathologies. Moreover, they readily fail to learn minority pathologies due to imbalanced training data. We propose two prompting strategies for MLVLMs that reduce hallucination and improve VQA performance. In the first strategy, we provide a detailed explanation of the queried pathology. In the second strategy, we fine-tune a cheap, weak learner to achieve high performance on a specific metric, and textually provide its judgment to the MLVLM. Tested on the MIMIC-CXR-JPG and Chexpert datasets, our methods significantly improve the diagnostic F1 score, with the highest increase being 0.27. We also demonstrate that our prompting strategies can be extended to general LVLM domains. Based on POPE metrics, it effectively suppresses the false negative predictions of existing LVLMs and improves Recall by approximately 0.07.

Related papers

• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions. We propose a novel approach utilizing structured medical reasoning. Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)
• MedHEval: Benchmarking Hallucinations and Mitigation Strategies in Medical Large Vision-Language Models [37.78272983522441]
  Large Vision Language Models (LVLMs) are becoming increasingly important in the medical domain. MedHEval is a novel benchmark that systematically evaluates hallucinations and mitigation strategies in Med-LVLMs. We conduct experiments across 11 popular (Med)-LVLMs and evaluate 7 state-of-the-art hallucination mitigation techniques.
  arXiv  Detail & Related papers  (2025-03-04T00:40:09Z)
• Med-R$^2$: Crafting Trustworthy LLM Physicians through Retrieval and Reasoning of Evidence-Based Medicine [39.80703772263271]
  We introduce Med-R2, a novel framework for Large Language Models (LLMs) that adheres to the Evidence-Based Medicine (EBM) process. Our experiments indicate that Med-R2 achieves a 14.87% improvement over vanilla RAG methods and even a 3.59% enhancement compared to fine-tuning strategies.
  arXiv  Detail & Related papers  (2025-01-21T04:40:43Z)
• Training Medical Large Vision-Language Models with Abnormal-Aware Feedback [57.98393950821579]
  We propose a novel UMed-LVLM designed with Unveiling Medical abnormalities. We propose a prompt method utilizing the GPT-4V to generate diagnoses based on identified abnormal areas in medical images. Experimental results demonstrate that our UMed-LVLM surpasses existing Med-LVLMs in identifying and understanding medical abnormality.
  arXiv  Detail & Related papers  (2025-01-02T17:37:20Z)
• Mitigating Hallucinations in Large Vision-Language Models (LVLMs) via Language-Contrastive Decoding (LCD) [13.430637580980164]
  Large Vision-Language Models (LVLMs) are an extension of Large Language Models (LLMs) that facilitate processing both image and text inputs, expanding AI capabilities. Our study introduces a Language Contrastive Decoding (LCD) algorithm that adjusts LVLM outputs based on Large Language Models distribution confidence levels. Our method effectively improves LVLMs without needing complex post-processing or retraining, and is easily applicable to different models.
  arXiv  Detail & Related papers  (2024-08-06T08:10:34Z)
• MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context [21.562034852024272]
  Large Vision Language Models (LVLMs) have recently achieved superior performance in various tasks on natural image and text data. Despite their advancements, there has been scant research on the robustness of these models against hallucination when fine-tuned on smaller datasets. We introduce a new benchmark dataset, the Medical Visual Hallucination Test (MedVH), to evaluate the hallucination of domain-specific LVLMs.
  arXiv  Detail & Related papers  (2024-07-03T00:59:03Z)
• OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM [48.16696073640864]
  We introduce OmniMedVQA, a novel comprehensive medical Visual Question Answering (VQA) benchmark. All images in this benchmark are sourced from authentic medical scenarios. We have found that existing LVLMs struggle to address these medical VQA problems effectively.
  arXiv  Detail & Related papers  (2024-02-14T13:51:56Z)
• Large Language Model Distilling Medication Recommendation Model [61.89754499292561]
  We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs) Our research aims to transform existing medication recommendation methodologies using LLMs. To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
  arXiv  Detail & Related papers  (2024-02-05T08:25:22Z)
• Beyond Task Performance: Evaluating and Reducing the Flaws of Large Multimodal Models with In-Context Learning [105.77733287326308]
  We evaluate 10 recent open-source LMMs from 3B up to 80B parameter scale, on 5 different axes; hallucinations, abstention, compositionality, explainability and instruction following. We explore the training-free in-context learning (ICL) as a solution, and study how it affects these limitations. Based on our ICL study, (3) we push ICL further and propose new multimodal ICL variants such as; Multitask-ICL, Chain-of-Hindsight-ICL, and Self-Correcting-ICL.
  arXiv  Detail & Related papers  (2023-10-01T12:02:59Z)
• MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering [45.84961106102445]
  Large Language Models (LLMs) often perform poorly on domain-specific tasks such as medical question answering (QA) We propose a comprehensive retrieval strategy to extract medical facts from an external knowledge base, and then inject them into the LLM's query prompt. Our retrieval-augmented Vicuna-7B model exhibited an accuracy improvement from 44.46% to 48.54%.
  arXiv  Detail & Related papers  (2023-09-27T21:26:03Z)
• Evaluation and Analysis of Hallucination in Large Vision-Language Models [49.19829480199372]
  Large Vision-Language Models (LVLMs) have recently achieved remarkable success. LVLMs are still plagued by the hallucination problem. Hallucination refers to the information of LVLMs' responses that does not exist in the visual input.
  arXiv  Detail & Related papers  (2023-08-29T08:51:24Z)
• Evaluating Object Hallucination in Large Vision-Language Models [122.40337582958453]
  This work presents the first systematic study on object hallucination of large vision-language models (LVLMs) We find that LVLMs tend to generate objects that are inconsistent with the target images in the descriptions. We propose a polling-based query method called POPE to evaluate the object hallucination.
  arXiv  Detail & Related papers  (2023-05-17T16:34:01Z)

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.