文章基本信息

标题：A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research
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作者：Marc Aubreville ; Christof A. Bertram ; Taryn A. Donovan 等
期刊名称：Scientific Data
电子版ISSN：2052-4463
出版年度：2020
卷号：7
期号：1
页码：1-10
DOI：10.1038/s41597-020-00756-z
语种：English
出版社：Nature Publishing Group
摘要：Canine mammary carcinoma (CMC) has been used as a model to investigate the pathogenesis of human breast cancer and the same grading scheme is commonly used to assess tumor malignancy in both. One key component of this grading scheme is the density of mitotic figures (MF). Current publicly available datasets on human breast cancer only provide annotations for small subsets of whole slide images (WSIs). We present a novel dataset of 21 WSIs of CMC completely annotated for MF. For this, a pathologist screened all WSIs for potential MF and structures with a similar appearance. A second expert blindly assigned labels, and for non-matching labels, a third expert assigned the final labels. Additionally, we used machine learning to identify previously undetected MF. Finally, we performed representation learning and two-dimensional projection to further increase the consistency of the annotations. Our dataset consists of 13,907 MF and 36,379 hard negatives. We achieved a mean F1-score of 0.791 on the test set and of up to 0.696 on a human breast cancer dataset.