Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning

Invasive ductal carcinoma (IDC) is the most common form of breast cancer. For the non-operative diagnosis of breast carcinoma, core needle biopsy has been widely used in recent years for the evaluation of histopathological features, as it can provide a definitive diagnosis between IDC and benign les...

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Autores principales:	Fahdi Kanavati, Masayuki Tsuneki
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	breast invasive ductal carcinoma deep learning weakly-supervised learning transfer learning whole slide image Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/bedf7aee21894d6381efdee2e9c6cae4
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id	oai:doaj.org-article:bedf7aee21894d6381efdee2e9c6cae4
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spelling	oai:doaj.org-article:bedf7aee21894d6381efdee2e9c6cae42021-11-11T15:29:15ZBreast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning10.3390/cancers132153682072-6694https://doaj.org/article/bedf7aee21894d6381efdee2e9c6cae42021-10-01T00:00:00Zhttps://www.mdpi.com/2072-6694/13/21/5368https://doaj.org/toc/2072-6694Invasive ductal carcinoma (IDC) is the most common form of breast cancer. For the non-operative diagnosis of breast carcinoma, core needle biopsy has been widely used in recent years for the evaluation of histopathological features, as it can provide a definitive diagnosis between IDC and benign lesion (e.g., fibroadenoma), and it is cost effective. Due to its widespread use, it could potentially benefit from the use of AI-based tools to aid pathologists in their pathological diagnosis workflows. In this paper, we trained invasive ductal carcinoma (IDC) whole slide image (WSI) classification models using transfer learning and weakly-supervised learning. We evaluated the models on a core needle biopsy (<i>n</i> = 522) test set as well as three surgical test sets (<i>n</i> = 1129) obtaining ROC AUCs in the range of 0.95–0.98. The promising results demonstrate the potential of applying such models as diagnostic aid tools for pathologists in clinical practice.Fahdi KanavatiMasayuki TsunekiMDPI AGarticlebreastinvasive ductal carcinomadeep learningweakly-supervised learningtransfer learningwhole slide imageNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancers, Vol 13, Iss 5368, p 5368 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	breast invasive ductal carcinoma deep learning weakly-supervised learning transfer learning whole slide image Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	breast invasive ductal carcinoma deep learning weakly-supervised learning transfer learning whole slide image Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Fahdi Kanavati Masayuki Tsuneki Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning
description	Invasive ductal carcinoma (IDC) is the most common form of breast cancer. For the non-operative diagnosis of breast carcinoma, core needle biopsy has been widely used in recent years for the evaluation of histopathological features, as it can provide a definitive diagnosis between IDC and benign lesion (e.g., fibroadenoma), and it is cost effective. Due to its widespread use, it could potentially benefit from the use of AI-based tools to aid pathologists in their pathological diagnosis workflows. In this paper, we trained invasive ductal carcinoma (IDC) whole slide image (WSI) classification models using transfer learning and weakly-supervised learning. We evaluated the models on a core needle biopsy (<i>n</i> = 522) test set as well as three surgical test sets (<i>n</i> = 1129) obtaining ROC AUCs in the range of 0.95–0.98. The promising results demonstrate the potential of applying such models as diagnostic aid tools for pathologists in clinical practice.
format	article
author	Fahdi Kanavati Masayuki Tsuneki
author_facet	Fahdi Kanavati Masayuki Tsuneki
author_sort	Fahdi Kanavati
title	Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning
title_short	Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning
title_full	Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning
title_fullStr	Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning
title_full_unstemmed	Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning
title_sort	breast invasive ductal carcinoma classification on whole slide images with weakly-supervised and transfer learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bedf7aee21894d6381efdee2e9c6cae4
work_keys_str_mv	AT fahdikanavati breastinvasiveductalcarcinomaclassificationonwholeslideimageswithweaklysupervisedandtransferlearning AT masayukitsuneki breastinvasiveductalcarcinomaclassificationonwholeslideimageswithweaklysupervisedandtransferlearning
_version_	1718435266468773888

Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning

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