Results 1 to 10 of about 22,354 (110)

BPI-MVQA: a bi-branch model for medical visual question answering [PDF]

BMC Medical Imaging, 2022
Background Visual question answering in medical domain (VQA-Med) exhibits great potential for enhancing confidence in diagnosing diseases and helping patients better understand their medical conditions.
Shengyan Liu, Xuejie Zhang, Xiaobing Zhou, Jian Yang   +3 more
doaj   +2 more sources

Vision–Language Model for Visual Question Answering in Medical Imagery

Bioengineering, 2023
In the clinical and healthcare domains, medical images play a critical role. A mature medical visual question answering system (VQA) can improve diagnosis by answering clinical questions presented with a medical image.
Yakoub Bazi, Mohamad Mahmoud Al Rahhal, Laila Bashmal, Mansour Zuair   +3 more
doaj   +3 more sources

Development of a large-scale medical visual question-answering dataset [PDF]

Communications Medicine
Background Medical Visual Question Answering (MedVQA) enhances diagnostic accuracy and healthcare delivery by leveraging artificial intelligence to interpret medical images.
Xiaoman Zhang, Chaoyi Wu, Ziheng Zhao, Weixiong Lin, Ya Zhang, Yanfeng Wang, Weidi Xie   +6 more
doaj   +2 more sources

ECSA: Mitigating Catastrophic Forgetting and Few-Shot Generalization in Medical Visual Question Answering [PDF]

Tomography
Objective: Medical Visual Question Answering (Med-VQA), a key technology that integrates computer vision and natural language processing to assist in clinical diagnosis, possesses significant potential for enhancing diagnostic efficiency and accuracy ...
Qinhao Jia, Shuxian Liu, Mingliang Chen, Tianyi Li, Jing Yang   +4 more
doaj   +2 more sources

A linguistic lens into vision-language models for open-ended question-answers in medical visual question answering [PDF]

Digital Health
Objectives Medical Visual Question Answering (MedVQA) systems are predominantly evaluated using exact-match accuracy, which fails to account for partially correct or clinically insightful answers, particularly in open-ended question settings.
Aiman Lameesa, Md Sakib Bin Alam, Khandaker Mohammad Mohi Uddin, Abdul Kadar Muhammad Masum   +3 more
doaj   +2 more sources

D²MNet: Difference-Aware Decoupling and Multi-Prompt Learning for Medical Difference Visual Question Answering [PDF]

Journal of Imaging
Difference visual question answering (Diff-VQA) aims to answer questions by identifying and reasoning about differences between medical images. Existing methods often rely on simple feature subtraction or fusion to model image differences, while ...
Lingge Lai, Weihua Ou, Jianping Gou, Zhonghua Liu   +3 more
doaj   +2 more sources

Evaluating Bard Gemini Pro and GPT-4 Vision Against Student Performance in Medical Visual Question Answering: Comparative Case Study [PDF]

JMIR Formative Research
BackgroundThe rapid development of large language models (LLMs) such as OpenAI’s ChatGPT has significantly impacted medical research and education. These models have shown potential in fields ranging from radiological imaging interpretation to medical ...
Jonas Roos, Ron Martin, Robert Kaczmarczyk   +2 more
doaj   +2 more sources

Vision-language models for medical report generation and visual question answering: a review

Frontiers in Artificial Intelligence
Medical vision-language models (VLMs) combine computer vision (CV) and natural language processing (NLP) to analyze visual and textual medical data. Our paper reviews recent advancements in developing VLMs specialized for healthcare, focusing on publicly
Iryna Hartsock, Ghulam Rasool
doaj   +3 more sources

PeFoMed: Parameter efficient fine-tuning of multimodal large language models for medical CXR [PDF]

Scientific Reports
Multimodal large language models (MLLMs) represent an evolutionary expansion in the capabilities of traditional large language models, enabling them to tackle challenges that surpass the scope of purely text-based applications.
Gang Liu, Xiaotian Tang, Jinlong He, Pengfei Li, Zhaolin Chen, Shenjun Zhong   +5 more
doaj   +2 more sources

Correction: Evaluating Bard Gemini Pro and GPT-4 Vision Against Student Performance in Medical Visual Question Answering: Comparative Case Study [PDF]

JMIR Formative Research
Jonas Roos, Ron Martin, Robert Kaczmarczyk   +2 more
doaj   +2 more sources