bibtext: @InProceedings{ Du_DistributionallyAdaptive_MICCAI2024,
                   author = { Du, Jing and Dong, Guangwei and Ma, Congbo and Xue, Shan and Wu, Jia and Yang, Jian and Beheshti, Amin and Sheng, Quan Z. and Giral, Alexis },
                   title = { { Distributionally-Adaptive Variational Meta Learning for Brain Graph Classification } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
abstract: Recent developments in Graph Neural Networks~(GNNs) have shed light on understanding brain networks through innovative approaches. Despite these innovations, the significant costs associated with data collection and the challenges posed by data drift in real-world scenarios present substantial hurdles for models dependent on large datasets to capture brain activity features. 
  To address these issues, we introduce the Distributionally-Adaptive Variational Meta Learning (DAML) framework, 
  designed to equip the model with rapid adaptability to varying distributions by meta-learning-driven minimization of discrepancies between subject sets. Initially, we employ a graph encoder with the message-passing strategy to generate precise brain graph representations. Subsequently, we implement a distributionally-adaptive variational meta learning approach to functionally simulate data drift across subject sets, utilizing variational layers for parameterization and adaptive alignment methods to reduce discrepancies. Through comprehensive experiments on three real-world datasets with both few-shot and standard settings against various baselines, our DAML model demonstrates the state-of-the-art performance across all metrics, underscoring its efficiency and potential within limited data.
title: Distributionally-Adaptive Variational Meta Learning for Brain Graph Classification
authors: Du, Jing Dong, Guangwei Ma, Congbo Xue, Shan Wu, Jia Yang, Jian Beheshti, Amin Sheng, Quan Z. Giral, Alexis