Glioma Classification Using Multimodal Radiology and Histology Data

Azam Hamidinekoo*, Tomasz Pieciak, Maryam Afzali, Otar Akanyeti, Yinyin Yuan

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

3 Dyfyniadau(SciVal)

Crynodeb

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.

Iaith wreiddiolSaesneg
TeitlBrainlesion
Is-deitlGlioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers
GolygyddionAlessandro Crimi, Spyridon Bakas
CyhoeddwrSpringer Nature
Tudalennau508-518
Nifer y tudalennau11
ISBN (Electronig)9783030720872
ISBN (Argraffiad)9783030720865
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 26 Maw 2021
Digwyddiad6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020 - Virtual, Online
Hyd: 04 Hyd 202004 Hyd 2020

Cyfres gyhoeddiadau

EnwLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Cyfrol12659 LNCS
ISSN (Argraffiad)0302-9743
ISSN (Electronig)1611-3349

Cynhadledd

Cynhadledd6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020
DinasVirtual, Online
Cyfnod04 Hyd 202004 Hyd 2020

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