Direct cellularity estimation on breast cancer histopathology images using transfer learning
type: Item
instance of: scholarly article
title: Direct cellularity estimation on breast cancer histopathology images using transfer learning
zbMATH Open document ID: 1423.92171
DOI: 10.1155/2019/3041250
author: Ziang Pei
author: Shuangliang Cao
author: Lijun Lu
author: Wufan Chen
published in: Computational \& Mathematical Methods in Medicine
publication date: 2019-07-09T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 92C55
Mathematics Subject Classification ID: 92-08
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62P10
zbMATH DE Number: 7077758
zbMATH Keywords: breast cancer histopathology images
zbMATH Keywords: cellularity estimation
zbMATH Keywords: transfer learning
Wikidata QID: Q91689192
describes a project that uses: Scikit
describes a project that uses: LightGBM
describes a project that uses: Xception
describes a project that uses: XGBoost
describes a project that uses: ImageNet
describes a project that uses: LIBSVM
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2019/3041250
OpenAlex ID: W2949003314
cites work: Greedy function approximation: A gradient boosting machine.
cites work: Q4856771
cites work: THE TREATMENT OF TIES IN RANKING PROBLEMS
cites work: A NEW MEASURE OF RANK CORRELATION
cites work: Q5396710
DBLP publication ID: journals/cmmm/PeiCLC19
description: scientific article; zbMATH DE number 7077758