bibtext: @InProceedings{ Jeo_BrainWaveNet_MICCAI2024,
                   author = { Jeong, Ah-Yeong and Heo, Da-Woon and Kang, Eunsong and Suk, Heung-Il },
                   title = { { BrainWaveNet: Wavelet-based Transformer for Autism Spectrum Disorder Diagnosis } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15002 },
                   month = {October},
                   pages = { pending },
                }
abstract: The diagnosis of Autism Spectrum Disorder (ASD) using resting-state functional Magnetic Resonance Imaging (rs-fMRI) is commonly analyzed through functional connectivity (FC) between Regions of Interest (ROIs) in the time domain. However, the time domain has limitations in capturing global information. To overcome this problem, we propose a wavelet-based Transformer, BrainWaveNet, that leverages the frequency domain and learns spatial-temporal information for rs-fMRI brain diagnosis. Specifically, BrainWaveNet learns inter-relations between two different frequency-based features (real and imaginary parts) by crossattention mechanisms, which allows for a deeper exploration of ASD. In our experiments using the ABIDE dataset, we validated the superiority of BrainWaveNet by comparing it with competing deep learning methods. Furthermore, we analyzed significant regions of ASD for neurological interpretation.In our experiments using the ABIDE dataset, we validated the superiority of BrainWaveNet by comparing with competing deep learning methods. Furthermore, we analyzed significant regions of ASD for neurological interpretation.
title: BrainWaveNet: Wavelet-based Transformer for Autism Spectrum Disorder Diagnosis
authors: Jeong, Ah-Yeong Heo, Da-Woon Kang, Eunsong Suk, Heung-Il