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Ghorbel, A.* ; Aldahdooh, A.* ; Albarqouni, S. ; Hamidouche, W.*

Transformer Based Models for Unsupervised Anomaly Segmentation in Brain MR Images.

In: (Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries). Berlin [u.a.]: Springer, 2023. 25-44 (Lect. Notes Comput. Sc. ; 13769 LNCS)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
The quality of patient care associated with diagnostic radiology is proportionate to a physician’s workload. Segmentation is a fundamental limiting precursor to both diagnostic and therapeutic procedures. Advances in machine learning (ML) aim to increase diagnostic efficiency by replacing a single application with generalized algorithms. The goal of unsupervised anomaly detection (UAD) is to identify potential anomalous regions unseen during training, where convolutional neural network (CNN) based autoencoders (AEs), and variational autoencoders (VAEs) are considered a de facto approach for reconstruction based-anomaly segmentation. The restricted receptive field in CNNs limits the CNN to model the global context. Hence, if the anomalous regions cover large parts of the image, the CNN-based AEs are not capable of bringing a semantic understanding of the image. Meanwhile, vision transformers (ViTs) have emerged as a competitive alternative to CNNs. It relies on the self-attention mechanism that can relate image patches to each other. We investigate in this paper Transformer’s capabilities in building AEs for the reconstruction-based UAD task to reconstruct a coherent and more realistic image. We focus on anomaly segmentation for brain magnetic resonance imaging (MRI) and present five Transformer-based models while enabling segmentation performance comparable to or superior to state-of-the-art (SOTA) models. The source code is made publicly available on GitHub.
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Publication type Article: Conference contribution
Corresponding Author
Keywords Anomaly Segmentation ; Deep-learning ; Neuroimaging ; Transformers ; Unsupervised Learning; Attention
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 13769 LNCS, Issue: , Pages: 25-44 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)