A fully-convolutional residual encoder-decoder neural network to localize breast cancer on histopathology images

Cancer detection in its early stages may allow patients to receive the proper treatment and save lives along with recovering the routine lifestyles. Breast cancer is of the top leading causes of mortality among women all around the globe. A source to find these cancerous nuclei is through analyzing...

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Vydané v:Computers in biology and medicine Ročník 147; s. 105698
Hlavní autori: Farajzadeh, Nacer, Sadeghzadeh, Nima, Hashemzadeh, Mahdi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 01.08.2022
Elsevier Limited
Predmet:
Accuracy
Algorithms
Automation
Biopsy
Breast cancer
Breast cancer nuclei
Classification
Coders
Datasets
Disease
Encoder-decoder
Encoders-Decoders
End-to-End learning
Fully convolutional neural networks
Histology
Histopathology
Hospitals
Image masking
Internal Medicine
Localization
Machine learning
Magnetic resonance imaging
Mammography
Medical imaging
Methods
Neural networks
Nuclei
Other
Patients
Residual networks
Tumors
United States > US
Wisconsin
Turkey
Residual networks
Breast cancer nuclei
End-to-End learning
Encoder-decoder
Fully convolutional neural networks
Image masking
ISSN:0010-4825, 1879-0534, 1879-0534
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Popis
Shrnutí:Cancer detection in its early stages may allow patients to receive the proper treatment and save lives along with recovering the routine lifestyles. Breast cancer is of the top leading causes of mortality among women all around the globe. A source to find these cancerous nuclei is through analyzing histopathology images. These images, however, are very complex and large. Thus, locating the cancerous nuclei in them is very challenging. Hence, if an expert fails to diagnose their patients via these images, the situation may be exacerbated. Therefore, this study aims to introduce a method to mask as many cancer nuclei on histopathology images as possible with a high visual aesthetic to make them distinguishable by experts easily. A tailored residual fully convolutional encoder-decoder neural network based on end-to-end learning is proposed to issue the matter. The proposed method is evaluated quantitatively and qualitatively on ER + BCa H&E-stained dataset. The average detection accuracy achieved by the method is 98.61%, which is much better than that of competitors. •A novel neural network is proposed to mask nuclei with 98.61% accuracy.•A post-processing procedure is offered to balance the network's output image.•The model in this study generalizes to a partially annotated dataset.•Augments data and improves accuracy by dividing histopathology images into blocks.•Endorses that a custom model instead of transfer learning is better in this realm.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2022.105698