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Found 6 papers, 4 papers with code
Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation
In this paper, we introduce a method that incorporates gradient-guided parameter perturbations to the visual encoder of the multimodality model during both pre-training and fine-tuning phases, to improve model generalization for downstream medical VQA tasks.
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
Self-supervised vision-language pretraining for Medical visual question answering
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 both vision and language information.
Ranked #1 on
Medical Visual Question Answering
on SLAKE-English
A comprehensive solution to retrieval-based chatbot construction
In this paper we present the results of our experiments in training and deploying a self-supervised retrieval-based chatbot trained with contrastive learning for assisting customer support agents.
Beating the bookies with their own numbers - and how the online sports betting market is rigged
The online sports gambling industry employs teams of data analysts to build forecast models that turn the odds at sports games in their favour.
Applications Other Computer Science Other Statistics
