BESTMVQA: A Benchmark Evaluation System for Medical Visual Question Answering.

所属单位：

School of Informatics, Xiamen University,

发表刊物：

ECML PKDD 2024

关键字：

"Medical Visual Question Answering";"Benchmark Evaluation System";"Comprehensive Empirical Study"

摘要：

Medical Visual Question Answering (Med-VQA) is a task that answers a natural language question with a medical image. Existing VQA techniques can be directly applied to solving the task. However, they often suffer from (i) the data insufficient problem, which makes it difficult to train the state of the arts (SOTAs) for domain-specific tasks, and (ii) the reproducibility problem, that existing models have not been thoroughly evaluated in a unified experimental setup. To address the issues, we develop a Benchmark Evaluation SysTem for Medical Visual Question Answering, denoted by BESTMVQA. Given clinical data, our system provides a useful tool for users to automatically build Med-VQA datasets. Users can conveniently select a wide spectrum of models from our library to perform a comprehensive evaluation study. With simple configurations, our system can automatically train and evaluate the selected models over a benchmark dataset, and reports the comprehensive results for users to develop new techniques or perform medical practice. Limitations of existing work are overcome (i) by the data generation tool, which automatically constructs new datasets from unstructured clinical data, and (ii) by evaluating SOTAs on benchmark datasets in a unified experimental setup. The demonstration video of our system can be found at https://youtu.be/QkEeFlu1x4A, and the source code is shared on https://github.com/emmali808/BESTMVQA.

第一作者：

Xiaojie Hong

通讯作者：

Xiaoli Wang

合写作者：

Zixin Song,Liang Zhi,Feiyan Liu

论文类型：

Original Research

学科门类：

医疗人工智能 / 计算机科学 / 多模态 / 视觉语言模型

文献类型：

Conference Paper

页面范围：

435-451

是否译文：

否

发表时间：

2024-08-22