BraTS2023-成人胶质瘤和BraTS-非洲
该数据集包含用于成人胶质瘤分割的多参数磁共振成像（MRI）扫描数据，涵盖T1、T1CE、T2和T2-FLAIR等多种影像模态，特别关注资源有限地区（如撒哈拉以南非洲）的临床需求。数据经过标准化预处理，适用于医学影像分析和机器学习模型开发，旨在提高胶质瘤的诊断准确性和医疗资源利用效率，支持相关算法在多样化医疗环境中的验证与应用。
基本信息
资源简介
BraTS2023-成人胶质瘤和BraTS-非洲
【数据集背景】
本数据集源站标题为《arXiv Query: search_query=&id_list=2412.04111v1&start=0&max_results=1》。源站首次发布：2024-12-05。
【数据内容】
源站原始描述：Gliomas, a kind of brain tumor characterized by high mortality, present substantial diagnostic challenges in low- and middle-income countries, particularly in Sub-Saharan Africa. This paper introduces a novel approach to glioma segmentation using transfer learning to address challenges in resource-limited regions with minimal and low-quality MRI data. We leverage pre-trained deep learning models, nnU-Net and MedNeXt, and apply a stratified fine-tuning strategy using the BraTS2023-Adult-Glioma an…
【主题与分类】
主题标签：cs.CV、eess.IV。
【获取与许可】
源站页面：https://arxiv.org/abs/2412.04111v1
下载信息
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使用方式
数据集说明
BraTS2023-成人胶质瘤和BraTS-非洲 对应论文数据集（arXiv 预印本）。
数据获取指引
- 打开论文页面获取作者与项目信息：https://arxiv.org/abs/2412.04111v1
- 论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
- 获取到实际数据链接后，按对应平台标准方式下载。
论文摘要：Abstract:Gliomas, a kind of brain tumor characterized by high mortality, present substantial diagnostic challenges in low- and middle-income countries, particularly in Sub-Saharan Africa. This paper introduces a novel approach to glioma segmentation using transfer learning to address challenges in resource-limited regions with minimal and low-quality MRI data. We leverage pre-trained deep learning models, nnU-Net and MedNeXt, and apply a stratified fine-tuning strategy using the BraTS2023-Adult-Glioma and BraTS-Africa datasets. Our method exploits radiomic analysis to create stratified training folds, model training on a large brain tumor dataset, and transfer learning to the Sub-Saharan context. A weighted model ensembling strategy and adaptive post-processing are employed to enhance segmentation accuracy. The evaluation of our proposed method on unseen validation cases on the BraTS-Africa 2024 task resulted in lesion-wise mean Dice scores of 0.870, 0.865, and 0.926, for enhancing tumor, tumor core, and whole tumor regions and was ranked first for the challenge. Our approach highlights the ability of integrated machine-learning techniques to bridge the gap between the medical imaging capabilities of resource-limited countries and established developed regions. By tailoring our methods to a target population's specific needs and constraints, we aim to enhance diagnostic capabilities in isolated environments. Our findings underscore the importance of approaches like local data integration and stratification refinement to address healthcare disparities, ensure practical a
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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