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High-resolution mapping of cells and tissue structures provides a foundationfor developing interpretable machine-learning models for computationalpathology. Deep learning algorithms can provide accurate mappings given largenumbers of labeled instances for training and validation. Generating adequatevolume of quality labels has emerged as a critical barrier in computationalpathology given the time and effort required from pathologists. In this paperwe describe an approach for engaging crowds of medical students andpathologists that was used to produce a dataset of over 220,000 annotations ofcell nuclei in breast cancers. We show how suggested annotations generated by aweak algorithm can improve the accuracy of annotations generated by non-expertsand can yield useful data for training segmentation algorithms withoutlaborious manual tracing. We systematically examine interrater agreement anddescribe modifications to the MaskRCNN model to improve cell mapping. We alsodescribe a technique we call Decision Tree Approximation of Learned Embeddings(DTALE) that leverages nucleus segmentations and morphologic features toimprove the transparency of nucleus classification models. The annotation dataproduced in this study are freely available for algorithm development andbenchmarking at: https://sites.google.com/view/nucls.

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