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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Veröffentlicht in:IEEE transactions on medical imaging Jg. 41; H. 12; S. 3952 - 3968
Hauptverfasser: Wang, Zhenzhen, Saoud, Carla, Wangsiricharoen, Sintawat, James, Aaron W., Popel, Aleksander S., Sulam, Jeremias
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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 only coarse and approximate annotations is a much easier task, less costly, and it alleviates pathologists' workload. In this paper, we study the problem of refining these approximate annotations in digital pathology to obtain more accurate ones. Some previous works have explored obtaining machine learning models from these inaccurate annotations, but few of them tackle the refinement problem where the mislabeled regions should be explicitly identified and corrected, and all of them require a - often very large - number of training samples. We present a method, named Label Cleaning Multiple Instance Learning (LC-MIL), to refine coarse annotations on a single WSI without the need for external training data. Patches cropped from a WSI with inaccurate labels are processed jointly within a multiple instance learning framework, mitigating their impact on the predictive model and refining the segmentation. Our experiments on a heterogeneous WSI set with breast cancer lymph node metastasis, liver cancer, and colorectal cancer samples show that LC-MIL significantly refines the coarse annotations, outperforming state-of-the-art alternatives, even while learning from a single slide. Moreover, we demonstrate how real annotations drawn by pathologists can be efficiently refined and improved by the proposed approach. All these results demonstrate that LC-MIL is a promising, lightweight tool to provide fine-grained annotations from coarsely annotated pathology sets.
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2022.3202759