Abstract

In fields like biomedical imaging, it is common to manage 3D images instead of 2D ones (CT-scans, MRI, 3D-ultrasound, etc.). Although 3D-Convolutional Neural Networks (CNNs) are generally more powerful compared to their 2D counterparts for such applications, it also comes at the cost of an increase in computational resources (both in time and memory). In this work, we present a new way to build 2D representations of 3D images while minimizing the information loss by leveraging quaternions. Those quaternion CNNs are able to offer competitive performance while significantly reducing computational complexity.
Original languageEnglish
Title of host publicationESANN 2024 Proceedings - The 32th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publication statusPublished - 2024

Publication series

NameESANN 2024 Proceedings

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