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. We show how suggested annotations generated by a
weak algorithm can improve the accuracy of annotations generated by non-experts
and can yield useful data for training segmentation algorithms without
laborious manual tracing. We systematically examine interrater agreement and
describe modifications to the MaskRCNN model to improve cell mapping. We also
describe a technique we call Decision Tree Approximation of Learned Embeddings
(DTALE) that leverages nucleus segmentations and morphologic features to
improve the transparency of nucleus classification models. The annotation data
produced in this study are freely available for algorithm development and
benchmarking at: this https URL

本文中，我们介绍了一种吸引医学生和病理学家参与的方法，用于在乳腺癌中标注细胞核的数据集，其中建议的注释可以通过弱算法来改善，该算法可用于训练分割算法，这种方法被称为 DTALE，旨在通过核分割和形态学特征来提高核分类模型的透明度。