MiDeSeC：乳腺癌组织病理学图像中有丝分裂检测与分割数据集
MiDeSeC: A Dataset for Mitosis Detection and Segmentation in Breast Cancer Histopathology Images
AI总结：
本文提出MiDeSeC数据集，基于25位乳腺癌患者H&E染色切片构建50个1024×1024区域、含500余个有丝分裂标注，用于有丝分裂检测与分割研究。
摘要：
MiDeSeC数据集通过安卡拉大学医学病理学系25位不同患者的H&E染色浸润性乳腺癌（非特殊类型，NST）切片构建，图像以40倍放大倍率采集。切片由3D Histech Panoramic p250 Flash-3扫描仪和Olympus BX50显微镜扫描。由于存在多种可能的有丝分裂形态，拥有一个覆盖所有情况的大型数据集至关重要。因此，从25位患者的玻璃切片中选取了总共50个区域，每个区域大小为1024×1024像素。这50个区域中总计包含超过500个有丝分裂。其中三分之二的区域保留用于训练，另外三分之一用于测试。
英文摘要：
The MiDeSeC dataset is created through H&E stained invasive breast carcinoma, no special type (NST) slides of 25 different patients captured at 40x magnification from the Department of Medical Pathology at Ankara University. The slides have been scanned by 3D Histech Panoramic p250 Flash-3 scanner and Olympus BX50 microscope. As several possible mitosis shapes exist, it is crucial to have a large dataset to cover all the cases. Accordingly, a total of 50 regions is selected from glass slides for 25 patients, each of regions with a size of 1024*1024 pixels. There are more than 500 mitoses in total in these 50 regions. Two-thirds of the regions are reserved for training, the other third for testing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
