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Medical Visual Question Answering

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Showing 51–97 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
Free Form Medical Visual Question Answering in Radiology—0
Hallucination Benchmark in Medical Visual Question AnsweringCode0
BESTMVQA: A Benchmark Evaluation System for Medical Visual Question Answering—0
A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical Image Analysis—0
Visual Question Answering in the Medical Domain—0
UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering—0
Q2ATransformer: Improving Medical VQA via an Answer Querying Decoder—0
Medical visual question answering using joint self-supervised learning—0
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning—0
UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering—0
MF2-MVQA: A Multi-stage Feature Fusion method for Medical Visual Question Answering—0
A Dual-Attention Learning Network with Word and Sentence Embedding for Medical Visual Question AnsweringCode0
RepsNet: Combining Vision with Language for Automated Medical Reports—0
OVQA: A Clinically Generated Visual Question Answering Dataset—0
ViLMedic: a framework for research at the intersection of vision and language in medical AICode0
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?—0
Medical Visual Question Answering: A Survey—0
V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL—0
MuVAM: A Multi-View Attention-based Model for Medical Visual Question Answering—0
Hierarchical Deep Multi-modal Network for Medical Visual Question AnsweringCode0
Leveraging Medical Visual Question Answering with Supporting Facts—0
A dataset of clinically generated visual questions and answers about radiology images—0
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