NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer.

ABSTRACT:

Background

Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and effort required from pathologists.

Results

This article describes a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei. We used this approach to produce the NuCLS dataset, containing >220,000 annotations of cell nuclei in breast cancers. This builds on prior work labeling tissue regions to produce an integrated tissue region- and cell-level annotation dataset for training that is the largest such resource

SUBMITTER: Amgad M 

PROVIDER: S-EPMC9112766 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature