Rethinking Radiology: An Analysis of Different Approaches to BraTS

This paper discusses the deep learning architectures currently used for pixel-wise segmentation of primary and secondary glioblastomas and low-grade gliomas. We implement various models such as the popular UNet architecture and compare the performance of these implementations on the BRATS dataset. T...

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Bibliographic Details
Published in:arXiv.org 2018-06
Main Authors: Bakst, William, Meyer-Teruel, Linus, Singh, Jasdeep
Format: Article
Language:English
Subjects:
Machine learning
Radiology
Segmentation
Online Access:Get full text
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Summary:This paper discusses the deep learning architectures currently used for pixel-wise segmentation of primary and secondary glioblastomas and low-grade gliomas. We implement various models such as the popular UNet architecture and compare the performance of these implementations on the BRATS dataset. This paper will explore the different approaches and combinations, offering an in depth discussion of how they perform and how we may improve upon them using more recent advancements in deep learning architectures.
ISSN:2331-8422