Grand Challenge MedFMC

MedFMC is a Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification. We aim at approaches adapting the foundation models for medical image classification and present a novel dataset and benchmark for the evaluation, i.e., examining the overall performance of accommodating the large-scale foundation models downstream on a set of diverse real-world clinical tasks. We collect five sets of medical imaging data from multiple institutes targeting a variety of real-world clinical tasks (22,349 images in total), i.e., thoracic diseases screening in X-rays, pathological tumor tissue screening, lesion detection in endoscopy images, neonatal jaundice evaluation, and diabetic retinopathy grading. Results of multiple baseline methods are demonstrated using the proposed dataset from both accuracy and cost-effective perspectives. We aim at examining the overall performance of accommodating large-scale foundation models downstream on a set of diverse real-world clinical tasks.
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MedFMC是一款面向医学图像分类任务的基础模型（Foundation Model）适配真实世界数据集与基准评测集。本研究旨在针对适配医学图像分类任务的基础模型方法展开探索，并构建了全新的数据集与基准评测集用于模型评估，即探究大规模基础模型在一系列多样化真实临床任务中完成下游适配的综合性能。本研究从多家医疗机构采集了五组针对不同真实临床任务的医学影像数据，总计22349张，具体涵盖胸部X线疾病筛查、病理肿瘤组织筛查、内镜影像病变检测、新生儿黄疸评估以及糖尿病视网膜病变分级。本研究基于该数据集开展了多种基线方法的实验验证，并从准确率与成本效益两个维度呈现了实验结果。本研究的核心目标仍是探究大规模基础模型在一系列多样化真实临床任务中进行下游适配的综合性能。所有咨询事宜请发送至邮箱[e-mail]与[e-mail]

搜集汇总

背景与挑战

背景概述

Grand Challenge MedFMC是一个真实世界的医疗图像分类数据集和基准，旨在评估基础模型在医疗图像分类中的适应性能。它包含五个医疗影像子集，覆盖多种临床任务，总计22,349张图像，由上海人工智能实验室·智慧医疗中心于2023年3月10日发布。