Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning

Im, Sanghyuk; Hyeon, Jonghwan; Rha, Eunyoung; Lee, Janghyeon; Choi, Ho-Jin; Jung, Yuchae; Kim, Tae-Jung

doi:10.3390/s21103500

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Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning

^1,†,

^2,†,

³,

²,

²,

^2,‡

and

Tae-Jung Kim

^4,*,‡

Department of Neurosurgery, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea

School of Computing, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea

Department of Plastic and Reconstructive Surgery, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea

⁴

Department of Hospital Pathology, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

^‡

These authors contributed equally to this work.

Academic Editors: Bogdan Smolka, Mehemmed Emre Celebi, Takahiko Horiuchi and Gianluigi Ciocca

Sensors 2021, 21(10), 3500; https://doi.org/10.3390/s21103500

Received: 9 April 2021 / Revised: 6 May 2021 / Accepted: 14 May 2021 / Published: 17 May 2021

(This article belongs to the Special Issue Sensors and Deep Learning for Digital Image Processing)

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Abstract

Diffuse gliomas are the most common primary brain tumors and they vary considerably in their morphology, location, genetic alterations, and response to therapy. In 2016, the World Health Organization (WHO) provided new guidelines for making an integrated diagnosis that incorporates both morphologic and molecular features to diffuse gliomas. In this study, we demonstrate how deep learning approaches can be used for an automatic classification of glioma subtypes and grading using whole-slide images that were obtained from routine clinical practice. A deep transfer learning method using the ResNet50V2 model was trained to classify subtypes and grades of diffuse gliomas according to the WHO’s new 2016 classification. The balanced accuracy of the diffuse glioma subtype classification model with majority voting was 0.8727. These results highlight an emerging role of deep learning in the future practice of pathologic diagnosis. View Full-Text

Keywords: digital pathology; deep transfer learning; convolutional neural network; oligodendroglial tumor; glioma

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Im, S.; Hyeon, J.; Rha, E.; Lee, J.; Choi, H.-J.; Jung, Y.; Kim, T.-J. Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning. Sensors 2021, 21, 3500. https://doi.org/10.3390/s21103500

AMA Style

Im S, Hyeon J, Rha E, Lee J, Choi H-J, Jung Y, Kim T-J. Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning. Sensors. 2021; 21(10):3500. https://doi.org/10.3390/s21103500

Chicago/Turabian Style

Im, Sanghyuk, Jonghwan Hyeon, Eunyoung Rha, Janghyeon Lee, Ho-Jin Choi, Yuchae Jung, and Tae-Jung Kim. 2021. "Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning" Sensors 21, no. 10: 3500. https://doi.org/10.3390/s21103500

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Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning

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