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Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images
[article]
2022
arXiv
pre-print
Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. However, generating exhaustive and accurate annotations is labor-intensive, challenging, and costly. Drawing only coarse and approximate annotations is a much easier task, less costly, and it alleviates pathologists' workload. In this paper, we study the problem of refining these approximate annotations in
arXiv:2109.10778v2
fatcat:5lnkdcqyszh75beeygnlhnudde