11 citations · 24 across the 5 of their papers we have counts for
5 papers
Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding
Jinlong He, Pengfei Li, Gang Liu +1
Multimodal Large Language Models (MLLMs) inherit the superior text understanding capabilities of LLMs and extend these capabilities to multimodal scenarios. These models achieve ex…
Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation
Gang Liu, Hongyang Li, Zerui He +1
Leveraging pre-trained visual language models has become a widely adopted approach for improving performance in downstream visual question answering (VQA) applications. However, in…
PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical Imaging
Jinlong He, Pengfei Li, Gang Liu +3
Multimodal large language models (MLLMs) represent an evolutionary expansion in the capabilities of traditional large language models, enabling them to tackle challenges that surpa…
Masked Vision and Language Pre-training with Unimodal and Multimodal Contrastive Losses for Medical Visual Question Answering
Pengfei Li, Gang Liu, Jinlong He +2
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 in…
Self-supervised vision-language pretraining for Medical visual question answering
Pengfei Li, Gang Liu, Lin Tan +2
Medical image visual question answering (VQA) is a task to answer clinical questions, given a radiographic image, which is a challenging problem that requires a model to integrate…