Grand_Challenge_MedFMC

displayName: Grand Challenge MedFMC
labelTypes: []
license: []
mediaTypes: []
publishDate: "2023-3-10"
publishUrl: https://opendatalab.org.cn/MedFMC
publisher:
- Shanghai Artificial Intelligence Laboratory
- MedicalAI
tags: []
taskTypes: []

---
# 数据集介绍
## 简介
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.
Please forward all the queries via [e-mail] and [e-mail]

## Download dataset
:modelscope-code[]{type="git"}

数据集显示名称：Grand Challenge MedFMC
标签类型：无
授权协议：无
媒体类型：无
发布日期：2023年3月10日
发布地址：https://opendatalab.org.cn/MedFMC
发布方：
- 上海人工智能实验室
- MedicalAI
标签：无
任务类型：无

---
# 数据集介绍
## 简介
MedFMC是一款面向医学图像分类领域基座模型（Foundation Model）适配的真实世界数据集与基准测试集。本研究旨在针对适配基座模型以完成医学图像分类的相关研究方法，并推出一款全新的数据集与基准测试集用于模型评估，即检验大规模基座模型在一系列多样化真实临床任务中进行下游适配的整体性能。本数据集从多家科研机构收集了针对五类真实临床任务的医学影像数据，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。本数据集从准确率与成本效益双维度，展示了多种基线方法的实验结果。本数据集的核心目标仍为检验大规模基座模型在多样化真实临床任务中进行下游适配的整体性能。
所有咨询事宜请联系邮箱：[e-mail] 与 [e-mail]

## 数据集下载
:modelscope-code[]{type="git"}