A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research

• URL: http://arxiv.org/abs/2008.10244v2
• Date: Fri, 27 Nov 2020 11:49:32 GMT
• Title: A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research
• Authors: Marc Aubreville, Christof A. Bertram, Taryn A. Donovan, Christian Marzahl, Andreas Maier, and Robert Klopfleisch
• Abstract summary: Current 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. We used machine learning to identify previously undetected MF.
• Score: 6.960375869417005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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