Structured crowdsourcing enables convolutional segmentation of histology images.

ABSTRACT:

Motivation

While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images.

Results

We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided b

SUBMITTER: Amgad M 

PROVIDER: S-EPMC6748796 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature