bibtext: @InProceedings{ Xue_WSSADN_MICCAI2024,
                   author = { Xue, Pengcheng and Nie, Dong and Zhu, Meijiao and Yang, Ming and Zhang, Han and Zhang, Daoqiang and Wen, Xuyun },
                   title = { { WSSADN: A Weakly Supervised Spherical Age-Disentanglement Network for Detecting Developmental Disorders with Structural MRI } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15011 },
                   month = {October},
                   pages = { pending },
                }
abstract: Structural magnetic resonance imaging characterizes the morphology and anatomical features of the brain and has been widely utilized in the diagnosis of developmental disorders. Given the dynamic nature of developmental disorder progression with age, existing methods for disease detection have incorporated age as either prior knowledge to be integrated or as a confounding factor to be disentangled through supervised learning. However, the excessive focus on age information in these methods restricts their capability to unearth disease-related features, thereby affecting the subsequent disease detection performance. To address this issue, this work introduces a novel weakly supervised learning-based method, namely, the Weakly Supervised Spherical Age Disentanglement Network (WSSADN). WSSADN innovatively combines an attention-based disentangler with the Conditional Generative Adversarial Network (CGAN) to remove normal developmental information from the brain representation of the patient with developmental disorder in a weakly supervised manner. By reducing the focus on age information during the disentanglement process, the effectiveness of the extracted disease-related features is enhanced, thereby increasing the accuracy of downstream disease identification. Moreover, to ensure effective convergence of the disentanglement and age information learning modules, we design a consistency regularization loss to align the age-related features generated by the disentangler and CGAN. We evaluated our method on three different tasks, including the detection of preterm neonates, infants with congenital heart disease, and autism spectrum disorders. The experimental results demonstrate that our method significantly outperforms existing state-of-the-art methods across all tasks.
title: WSSADN: A Weakly Supervised Spherical Age-Disentanglement Network for Detecting Developmental Disorders with Structural MRI
authors: Xue, Pengcheng Nie, Dong Zhu, Meijiao Yang, Ming Zhang, Han Zhang, Daoqiang Wen, Xuyun