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Medical Visual Question Answering

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Showing 51–75 of 97 papers

TitleStatusHype
DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels—0
ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical ImagesCode0
Alignment, Mining and Fusion: Representation Alignment with Hard Negative Mining and Selective Knowledge Fusion for Medical Visual Question Answering—0
GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis—0
Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model—0
Efficient Bilinear Attention-based Fusion for Medical Visual Question Answering—0
R-LLaVA: Improving Med-VQA Understanding through Visual Region of Interest—0
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering—0
LLaVA-Ultra: Large Chinese Language and Vision Assistant for Ultrasound—0
ZALM3: Zero-Shot Enhancement of Vision-Language Alignment via In-Context Information in Multi-Turn Multimodal Medical Dialogue—0
Kvasir-VQA: A Text-Image Pair GI Tract DatasetCode0
FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge InjectionCode0
Med-PMC: Medical Personalized Multi-modal Consultation with a Proactive Ask-First-Observe-Next ParadigmCode0
Targeted Visual Prompting for Medical Visual Question AnsweringCode0
Prompting Medical Large Vision-Language Models to Diagnose Pathologies by Visual Question Answering—0
TM-PATHVQA:90000+ Textless Multilingual Questions for Medical Visual Question Answering—0
Tri-VQA: Triangular Reasoning Medical Visual Question Answering for Multi-Attribute Analysis—0
Detecting and Evaluating Medical Hallucinations in Large Vision Language Models—0
Efficiency in Focus: LayerNorm as a Catalyst for Fine-tuning Medical Visual Language Pre-trained Models—0
Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering—0
Grounded Knowledge-Enhanced Medical VLP for Chest X-Ray—0
WangLab at MEDIQA-M3G 2024: Multimodal Medical Answer Generation using Large Language Models—0
MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationale—0
Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation—0
Prompt-based Personalized Federated Learning for Medical Visual Question Answering—0
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