Considerations for data acquisition and modeling strategies: Mitosis detection in computational pathologyDownload PDF

Published: 04 Apr 2023, Last Modified: 28 Apr 2023MIDL 2023 PosterReaders: Everyone
Keywords: computational pathology, mitosis detection, breast cancer
Abstract: Preparing data for machine learning tasks in health and life science applications requires decisions that affect the cost, model properties and performance. In this work, we study the implication of data collection strategies, focusing on a case study of mitosis detection. Specifically, we investigate the use of expert and crowd-sourced labelers, the impact of aggregated vs single labels, and the framing of the problem as either classification or object detection. Our results demonstrate the value of crowd-sourced labels, importance of uncertainty quantification, and utility of negative samples.
TL;DR: Crowd-sourced data, a measure of label uncertainty, and regression models perform well in a study of mitosis detection for computational pathology.
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