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2021 | OriginalPaper | Chapter

Cerberus: A Multi-headed Network for Brain Tumor Segmentation

Authors : Laura Daza, Catalina Gómez, Pablo Arbeláez

Published in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Publisher: Springer International Publishing

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Abstract

The chapter introduces Cerberus, a multi-headed network designed for brain tumor segmentation using MRI data. It addresses the challenges of annotating irregular tumor shapes and the scarcity of datasets. Cerberus uses a shared encoder to learn common representations and specialized decoders for different brain regions, combining their outputs to produce a multi-label segmentation. The model's architecture and training strategies are detailed, along with extensive experiments and comparisons with state-of-the-art methods. The results demonstrate Cerberus's competitive performance in the BraTS challenge, showcasing its ability to accurately segment brain tumors with a single, efficient network.
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Metadata
Title
Cerberus: A Multi-headed Network for Brain Tumor Segmentation
Authors
Laura Daza
Catalina Gómez
Pablo Arbeláez
Copyright Year
2021
Book
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Print ISBN: 978-3-030-72086-5

Electronic ISBN: 978-3-030-72087-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-72087-2_30

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