Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images

Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. However, generating exhaustive and accurate annotations is labor-intensive, challenging, and costly. Drawing o...

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Bibliographic Details
Published in:	IEEE transactions on medical imaging Vol. 41; no. 12; pp. 3952 - 3968
Main Authors:	Wang, Zhenzhen, Saoud, Carla, Wangsiricharoen, Sintawat, James, Aaron W., Popel, Aleksander S., Sulam, Jeremias
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Annotations Breast cancer Breast Neoplasms - pathology Cancer Cleaning coarse annotations Colorectal carcinoma Female Humans Image segmentation Impact prediction label cleaning Learning algorithms Liver cancer Lymph nodes Lymphatic Metastasis Machine Learning Medical imaging Medical research Metastases multiple instance learning Pathology Prediction models Predictive models Refining Training Tumors Whole-slide image segmentation
ISSN:	0278-0062, 1558-254X, 1558-254X
Online Access:	Get full text
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