Weakly-supervised learning for lung carcinoma classification using deep learning

Abstract Lung cancer is one of the major causes of cancer-related deaths in many countries around the world, and its histopathological diagnosis is crucial for deciding on optimum treatment strategies. Recently, Artificial Intelligence (AI) deep learning models have been widely shown to be useful in...

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Autores principales:	Fahdi Kanavati, Gouji Toyokawa, Seiya Momosaki, Michael Rambeau, Yuka Kozuma, Fumihiro Shoji, Koji Yamazaki, Sadanori Takeo, Osamu Iizuka, Masayuki Tsuneki
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ca955f6401a44b0690a1db64ed281447
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spelling	oai:doaj.org-article:ca955f6401a44b0690a1db64ed2814472021-12-02T17:52:33ZWeakly-supervised learning for lung carcinoma classification using deep learning10.1038/s41598-020-66333-x2045-2322https://doaj.org/article/ca955f6401a44b0690a1db64ed2814472020-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-66333-xhttps://doaj.org/toc/2045-2322Abstract Lung cancer is one of the major causes of cancer-related deaths in many countries around the world, and its histopathological diagnosis is crucial for deciding on optimum treatment strategies. Recently, Artificial Intelligence (AI) deep learning models have been widely shown to be useful in various medical fields, particularly image and pathological diagnoses; however, AI models for the pathological diagnosis of pulmonary lesions that have been validated on large-scale test sets are yet to be seen. We trained a Convolution Neural Network (CNN) based on the EfficientNet-B3 architecture, using transfer learning and weakly-supervised learning, to predict carcinoma in Whole Slide Images (WSIs) using a training dataset of 3,554 WSIs. We obtained highly promising results for differentiating between lung carcinoma and non-neoplastic with high Receiver Operator Curve (ROC) area under the curves (AUCs) on four independent test sets (ROC AUCs of 0.975, 0.974, 0.988, and 0.981, respectively). Development and validation of algorithms such as ours are important initial steps in the development of software suites that could be adopted in routine pathological practices and potentially help reduce the burden on pathologists.Fahdi KanavatiGouji ToyokawaSeiya MomosakiMichael RambeauYuka KozumaFumihiro ShojiKoji YamazakiSadanori TakeoOsamu IizukaMasayuki TsunekiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fahdi Kanavati Gouji Toyokawa Seiya Momosaki Michael Rambeau Yuka Kozuma Fumihiro Shoji Koji Yamazaki Sadanori Takeo Osamu Iizuka Masayuki Tsuneki Weakly-supervised learning for lung carcinoma classification using deep learning
description	Abstract Lung cancer is one of the major causes of cancer-related deaths in many countries around the world, and its histopathological diagnosis is crucial for deciding on optimum treatment strategies. Recently, Artificial Intelligence (AI) deep learning models have been widely shown to be useful in various medical fields, particularly image and pathological diagnoses; however, AI models for the pathological diagnosis of pulmonary lesions that have been validated on large-scale test sets are yet to be seen. We trained a Convolution Neural Network (CNN) based on the EfficientNet-B3 architecture, using transfer learning and weakly-supervised learning, to predict carcinoma in Whole Slide Images (WSIs) using a training dataset of 3,554 WSIs. We obtained highly promising results for differentiating between lung carcinoma and non-neoplastic with high Receiver Operator Curve (ROC) area under the curves (AUCs) on four independent test sets (ROC AUCs of 0.975, 0.974, 0.988, and 0.981, respectively). Development and validation of algorithms such as ours are important initial steps in the development of software suites that could be adopted in routine pathological practices and potentially help reduce the burden on pathologists.
format	article
author	Fahdi Kanavati Gouji Toyokawa Seiya Momosaki Michael Rambeau Yuka Kozuma Fumihiro Shoji Koji Yamazaki Sadanori Takeo Osamu Iizuka Masayuki Tsuneki
author_facet	Fahdi Kanavati Gouji Toyokawa Seiya Momosaki Michael Rambeau Yuka Kozuma Fumihiro Shoji Koji Yamazaki Sadanori Takeo Osamu Iizuka Masayuki Tsuneki
author_sort	Fahdi Kanavati
title	Weakly-supervised learning for lung carcinoma classification using deep learning
title_short	Weakly-supervised learning for lung carcinoma classification using deep learning
title_full	Weakly-supervised learning for lung carcinoma classification using deep learning
title_fullStr	Weakly-supervised learning for lung carcinoma classification using deep learning
title_full_unstemmed	Weakly-supervised learning for lung carcinoma classification using deep learning
title_sort	weakly-supervised learning for lung carcinoma classification using deep learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/ca955f6401a44b0690a1db64ed281447
work_keys_str_mv	AT fahdikanavati weaklysupervisedlearningforlungcarcinomaclassificationusingdeeplearning AT goujitoyokawa weaklysupervisedlearningforlungcarcinomaclassificationusingdeeplearning AT seiyamomosaki weaklysupervisedlearningforlungcarcinomaclassificationusingdeeplearning AT michaelrambeau weaklysupervisedlearningforlungcarcinomaclassificationusingdeeplearning AT yukakozuma weaklysupervisedlearningforlungcarcinomaclassificationusingdeeplearning AT fumihiroshoji weaklysupervisedlearningforlungcarcinomaclassificationusingdeeplearning AT kojiyamazaki weaklysupervisedlearningforlungcarcinomaclassificationusingdeeplearning AT sadanoritakeo weaklysupervisedlearningforlungcarcinomaclassificationusingdeeplearning AT osamuiizuka weaklysupervisedlearningforlungcarcinomaclassificationusingdeeplearning AT masayukitsuneki weaklysupervisedlearningforlungcarcinomaclassificationusingdeeplearning
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Weakly-supervised learning for lung carcinoma classification using deep learning

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