Supporting data for "NuCLS: A scalable crowdsourcing approach & dataset for nucleus classification and segmentation in breast cancer"

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. <br>This paper 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 over 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 for multi-scale analysis of breast cancer histology. This paper presents data and analysis results for single and multi-rater annotations from both non-experts and pathologists. We present a novel method for suggesting annotations that allows us to collect accurate segmentation data without the need for laborious manual tracing of cells. Our results indicate that even noisy algorithmic suggestions do not adversely affect pathologist accuracy, and can help non-experts improve annotation quality. We also present a new approach for inferring truth from multiple raters, and show that non-experts can produce accurate annotations for visually distinctive classes. <br>This study is the most extensive systematic exploration of the large-scale use of wisdom-of-the-crowd approaches to generate data for computational pathology applications.<br>The NuCLS study utilises TCGA data. The TCGA group established informed consent guidelines for effective and fair use of cancer genomic information. The following link details TCGA Ethics &amp; Policies, including TCGA informed consent policy, data access policy and information about HIPAA Privacy Rule compliance: https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/history/policies