Papers Medical Visual Question Answering
“Medical Visual Question Answering” 태그가 달린 논문 97편 · 필터 해제
Barriers in Integrating Medical Visual Question Answering into Radiology Workflows: A Scoping Review and Clinicians' Insights
Medical Visual Question Answering (MedVQA) is a promising tool to assist radiologists by automating medical image interpretation through question answering. Despite advances in models and datasets, MedVQA's integration i…
DiagnosticMedical Visual Question AnsweringQuestion AnsweringVisual Question AnsweringSMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning
Multimodal in-context learning (ICL) remains underexplored despite significant potential for domains such as medicine. Clinicians routinely encounter diverse, specialized tasks requiring adaptation from limited examples,…
In-Context LearningMedical Visual Question AnsweringQuestion AnsweringVisual Question Answering+1GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning
Medical visual question answering aims to support clinical decision-making by enabling models to answer natural language questions based on medical images. While recent advances in multi-modal learning have significantly…
Answer GenerationDecision MakingMedical Visual Question AnsweringQuestion Answering+3CAPO: Reinforcing Consistent Reasoning in Medical Decision-Making
In medical visual question answering (Med-VQA), achieving accurate responses relies on three critical steps: precise perception of medical imaging data, logical reasoning grounded in visual input and textual questions, a…
Answer GenerationDecision MakingLogical ReasoningMedical Visual Question Answering+4Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal Endoscopy
Medical Visual Question Answering (MedVQA) is a promising field for developing clinical decision support systems, yet progress is often limited by the available datasets, which can lack clinical complexity and visual div…
Medical Visual Question AnsweringQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)MedOrch: Medical Diagnosis with Tool-Augmented Reasoning Agents for Flexible Extensibility
Healthcare decision-making represents one of the most challenging domains for Artificial Intelligence (AI), requiring the integration of diverse knowledge sources, complex reasoning, and various external analytical tools…
Decision MakingMedical DiagnosisMedical Visual Question AnsweringQuestion Answering+1A Causal Approach to Mitigate Modality Preference Bias in Medical Visual Question Answering
Medical Visual Question Answering (MedVQA) is crucial for enhancing the efficiency of clinical diagnosis by providing accurate and timely responses to clinicians' inquiries regarding medical images. Existing MedVQA model…
counterfactualMedical Visual Question AnsweringQuestion AnsweringVisual Question Answering+1Toward Effective Reinforcement Learning Fine-Tuning for Medical VQA in Vision-Language Models
Recently, reinforcement learning (RL)-based tuning has shifted the trajectory of Multimodal Large Language Models (MLLMs), particularly following the introduction of Group Relative Policy Optimization (GRPO). However, di…
Medical Visual Question AnsweringQuestion AnsweringReinforcement Learning (RL)Visual Question Answering+1MedAgentBoard: Benchmarking Multi-Agent Collaboration with Conventional Methods for Diverse Medical Tasks
The rapid advancement of Large Language Models (LLMs) has stimulated interest in multi-agent collaboration for addressing complex medical tasks. However, the practical advantages of multi-agent collaboration approaches r…
BenchmarkingMedical Visual Question AnsweringQuestion AnsweringVisual Question Answering+1Structure Causal Models and LLMs Integration in Medical Visual Question Answering
Medical Visual Question Answering (MedVQA) aims to answer medical questions according to medical images. However, the complexity of medical data leads to confounders that are difficult to observe, so bias between images …
Causal InferenceMedical Visual Question AnsweringQuestion AnsweringVisual Question AnsweringBridging the Semantic Gaps: Improving Medical VQA Consistency with LLM-Augmented Question Sets
Medical Visual Question Answering (MVQA) systems can interpret medical images in response to natural language queries. However, linguistic variability in question phrasing often undermines the consistency of these system…
DiversityMedical Visual Question AnsweringNatural Language QueriesQuestion Answering+3Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion
Medical Visual Question Answering (Med-VQA) answers clinical questions using medical images, aiding diagnosis. Designing the MedVQA system holds profound importance in assisting clinical diagnosis and enhancing diagnosti…
DiagnosticMedical Visual Question AnsweringQuestion AnsweringVisual Question Answering+1Vision-Amplified Semantic Entropy for Hallucination Detection in Medical Visual Question Answering
Multimodal large language models (MLLMs) have demonstrated significant potential in medical Visual Question Answering (VQA). Yet, they remain prone to hallucinations-incorrect responses that contradict input images, posi…
DiagnosticHallucinationMedical Visual Question AnsweringQuestion Answering+2DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels
Medical Visual Question Answering (Med-VQA) systems benefit the interpretation of medical images containing critical clinical information. However, the challenge of noisy labels and limited high-quality datasets remains …
Medical Visual Question AnsweringQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical Images
Medical Visual Question Answering (Med-VQA) represents a critical and challenging subtask within the general VQA domain. Despite significant advancements in general Visual Question Answering (VQA), multimodal large langu…
Clinical KnowledgeMedical Visual Question AnsweringQuestion AnsweringVisual Question Answering+1Alignment, Mining and Fusion: Representation Alignment with Hard Negative Mining and Selective Knowledge Fusion for Medical Visual Question Answering
Medical Visual Question Answering (Med-VQA) is a challenging task that requires a deep understanding of both medical images and textual questions. Although recent works leveraging Medical Vision-Language Pre-training…
Contrastive LearningMedical Visual Question AnsweringQuestion AnsweringVisual Question Answering+1MedCoT: Medical Chain of Thought via Hierarchical Expert
Artificial intelligence has advanced in Medical Visual Question Answering (Med-VQA), but prevalent research tends to focus on the accuracy of the answers, often overlooking the reasoning paths and interpretability, which…
DiagnosticMedical Visual Question AnsweringMixture-of-ExpertsQuestion Answering+2BiMediX2: Bio-Medical EXpert LMM for Diverse Medical Modalities
This paper introduces BiMediX2, a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model (LMM) with a unified architecture that integrates text and visual modalities, enabling advanced image understanding a…
Medical Visual Question AnsweringQuestion AnsweringVisual Question AnsweringGEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis
Medical Visual Question Answering (VQA) is an essential technology that integrates computer vision and natural language processing to automatically respond to clinical inquiries about medical images. However, current med…
Medical Visual Question AnsweringMultiple-choiceQuestion AnsweringVisual Question Answering+1A Survey of Medical Vision-and-Language Applications and Their Techniques
Medical vision-and-language models (MVLMs) have attracted substantial interest due to their capability to offer a natural language interface for interpreting complex medical data. Their applications are versatile and hav…
Decision MakingDiagnosticImage-text RetrievalMedical Report Generation+5