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Found 2 papers, 2 papers with code
PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical Imaging
In this paper, we propose a parameter efficient framework for fine-tuning MLLMs, specifically validated on medical visual question answering (Med-VQA) and medical report generation (MRG) tasks, using public benchmark datasets.
Ranked #1 on
Medical Visual Question Answering
on VQA-RAD
(using extra training data)
Medical Report Generation
Medical Visual Question Answering
+4
Masked Vision and Language Pre-training with Unimodal and Multimodal Contrastive Losses for Medical Visual Question Answering
Medical visual question answering (VQA) is a challenging task that requires answering clinical questions of a given medical image, by taking consider of both visual and language information.
Ranked #1 on
Medical Visual Question Answering
on PathVQA
