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场景下基础模型（Foundation Model）适配任务的真实世界数据集与基准测试集。本研究旨在为适配医学图像分类任务的基础模型提供研究支撑，并构建了全新的数据集与基准测试集以开展评估工作，即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。本研究从多家机构采集了针对多种真实临床任务的五组医学影像数据，总计22349张图像，具体包括：X射线胸部疾病筛查、病理肿瘤组织筛查、内窥镜图像病变检测、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展示了实验结果。本研究旨在评估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。所有咨询事宜请通过[e-mail]与[e-mail]联系。

搜集汇总

背景与挑战

背景概述

MedFMC是一个用于医学图像分类的真实世界数据集和基准，旨在评估基础模型在多样化临床任务中的适应性能。它包含五个子集，总计22,349张图像，覆盖胸部X光疾病筛查、病理肿瘤组织检测、内窥镜病变分类、新生儿黄疸评估和糖尿病性视网膜病变分级等任务。该数据集支持通过少量样本提示来开发和测试基础模型的泛化能力。