Hamidinekoo, A, Pieciak, T, Afzali, M et al. (2 more authors) (2021) Glioma Classification Using Multimodal Radiology and Histology Data. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II. BrainLes: International MICCAI Brainlesion Workshop 2020, 04 Oct 2020, Lima, Peru. Springer , pp. 508-518. ISBN 9783030720865
Abstract
Gliomas are brain tumours with a high mortality rate. There are various grades and sub-types of this tumour, and the treatment procedure varies accordingly. Clinicians and oncologists diagnose and categorise these tumours based on visual inspection of radiology and histology data. However, this process can be time-consuming and subjective. The computer-assisted methods can help clinicians to make better and faster decisions. In this paper, we propose a pipeline for automatic classification of gliomas into three sub-types: oligodendroglioma, astrocytoma, and glioblastoma, using both radiology and histopathology images. The proposed approach implements distinct classification models for radiographic and histologic modalities and combines them through an ensemble method. The classification algorithm initially carries out tile-level (for histology) and slice-level (for radiology) classification via a deep learning method, then tile/slice-level latent features are combined for a whole-slide and whole-volume sub-type prediction. The classification algorithm was evaluated using the data set provided in the CPM-RadPath 2020 challenge. The proposed pipeline achieved the F1-Score of 0.886, Cohen’s Kappa score of 0.811 and Balance accuracy of 0.860. The ability of the proposed model for end-to-end learning of diverse features enables it to give a comparable prediction of glioma tumour sub-types.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Springer Nature Switzerland AG. This is an author produced version of a conference paper published in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Glioma classification; Digital pathology; Multimodal MRI |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Biomedical Imaging Science Dept (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Jun 2022 14:46 |
Last Modified: | 01 Jul 2022 00:13 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-030-72087-2_45 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:187979 |