bibtext: @InProceedings{ Li_Exploring_MICCAI2024,
                   author = { Li, Lanting and Zhang, Liuzeng and Cao, Peng and Yang, Jinzhu and Wang, Fei and Zaiane, Osmar R. },
                   title = { { Exploring Spatio-Temporal Interpretable Dynamic Brain Function with Transformer for Brain 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 dynamic variation in the spatio-temporal organizational patterns of brain functional modules (BFMs) associated with brain disorders remains unclear. To solve this issue, we propose an end-to-end transformer-based framework for sufficiently learning the spatio-temporal characteristics of BFMs and exploring the interpretable variation related to brain disorders. Specifically, the proposed model incorporates a supervisory guidance spatio-temporal clustering strategy for automatically identifying the BFMs with the dynamic temporal-varying weights and a multi-channel self-attention mechanism with topology-aware projection for sufficiently exploring the temporal variation and spatio-temporal representation. The experimental results on the diagnosis of Major Depressive Disorder (MDD) and Bipolar Disorder (BD) indicate that our model achieves state-of-the-art performance. Moreover, our model is capable of identifying the spatio-temporal patterns of brain activity and providing evidence associated with brain disorders. Our code is available at https://github.com/llt1836/BISTformer.
title: Exploring Spatio-Temporal Interpretable Dynamic Brain Function with Transformer for Brain Disorder Diagnosis
authors: Li, Lanting Zhang, Liuzeng Cao, Peng Yang, Jinzhu Wang, Fei Zaiane, Osmar R.