2024-01-23-ji24b.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Considerations for data acquisition and modeling strategies: Mitosis detection in computational pathology	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	ji24b	0	Considerations for data acquisition and modeling strategies: Mitosis detection in computational pathology	1051	1066	1051-1066	1051	false	Ji, Zongliang and Rosenfield, Philip and Eng, Christina and Bettigole, Sarah and Gibson, Danielle C and Masoudi, Hamid and Hanna, Matthew and Fusi, Nicolo and Severson, Kristen A	given family Zongliang Ji given family Philip Rosenfield given family Christina Eng given family Sarah Bettigole given family Danielle C Gibson given family Hamid Masoudi given family Matthew Hanna given family Nicolo Fusi given family Kristen A Severson	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/ji24b/ji24b.pdf