Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning
| Autor: | Sang-Hyuk Im, Eunyoung Rha, Yuchae Jung, Ho-Jin Choi, Janghyeon Lee, Jonghwan Hyeon, Tae-Jung Kim |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
convolutional neural network TP1-1185 World Health Organization Biochemistry Analytical Chemistry Machine Learning 03 medical and health sciences Diffuse Glioma 0302 clinical medicine Glioma glioma deep transfer learning Medicine Humans Oligodendroglial Tumor Electrical and Electronic Engineering Instrumentation Pathological Grading (tumors) 030304 developmental biology 0303 health sciences business.industry Brain Neoplasms Deep learning Chemical technology Communication Digital pathology medicine.disease oligodendroglial tumor Atomic and Molecular Physics and Optics Mutation Artificial intelligence Radiology business Transfer of learning digital pathology 030217 neurology & neurosurgery |
| Zdroj: | Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 3500, p 3500 (2021) |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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