Direct Cellularity Estimation on Breast Cancer Histopathology Images Using Transfer Learning
Joint Authors
Chen, Wufan
Pei, Ziang
Cao, Shuangliang
Lu, Lijun
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-09
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Residual cancer burden (RCB) has been proposed to measure the postneoadjuvant breast cancer response.
In the workflow of RCB assessment, estimation of cancer cellularity is a critical task, which is conventionally achieved by manually reviewing the hematoxylin and eosin- (H&E-) stained microscopic slides of cancer sections.
In this work, we develop an automatic and direct method to estimate cellularity from histopathological image patches using deep feature representation, tree boosting, and support vector machine (SVM), avoiding the segmentation and classification of nuclei.
Using a training set of 2394 patches and a test set of 185 patches, the estimations by our method show strong correlation to those by the human pathologists in terms of intraclass correlation (ICC) (0.94 with 95% CI of (0.93, 0.96)), Kendall’s tau (0.83 with 95% CI of (0.79, 0.86)), and the prediction probability (0.93 with 95% CI of (0.91, 0.94)), compared to two other methods (ICC of 0.74 with 95% CI of (0.70, 0.77) and 0.83 with 95% CI of (0.79, 0.86)).
Our method improves the accuracy and does not rely on annotations of individual nucleus.
American Psychological Association (APA)
Pei, Ziang& Cao, Shuangliang& Lu, Lijun& Chen, Wufan. 2019. Direct Cellularity Estimation on Breast Cancer Histopathology Images Using Transfer Learning. Computational and Mathematical Methods in Medicine،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1130519
Modern Language Association (MLA)
Pei, Ziang…[et al.]. Direct Cellularity Estimation on Breast Cancer Histopathology Images Using Transfer Learning. Computational and Mathematical Methods in Medicine No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1130519
American Medical Association (AMA)
Pei, Ziang& Cao, Shuangliang& Lu, Lijun& Chen, Wufan. Direct Cellularity Estimation on Breast Cancer Histopathology Images Using Transfer Learning. Computational and Mathematical Methods in Medicine. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1130519
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130519