NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation

Understand

High-resolution mapping of cells and tissue structures provides a foundation for developing interpretable machine-learning models for computational pathology.

• Deep learning algorithms can provide accurate mappings given large numbers of labeled instances for training and validation.
• Generating adequate volume of quality labels has emerged as a critical barrier in computational pathology given the time and effort required from pathologists.
• In this paper we describe an approach for engaging crowds of medical students and pathologists that was used to produce a dataset of over 220,000 annotations of cell nuclei in breast cancers.