Symmetric Dual Adversarial Connectomic Domain Alignment for Predicting Isomorphic Brain Graph from a Baseline Graph

Bessadok A., Mahjoub M. A., Rekık I.

10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Shenzhen, China, 13 - 17 October 2019, vol.11767, pp.465-474 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 11767
  • Doi Number: 10.1007/978-3-030-32251-9_51
  • City: Shenzhen
  • Country: China
  • Page Numbers: pp.465-474
  • Istanbul Technical University Affiliated: Yes


Medical image synthesis techniques can circumvent the need for costly clinical scan acquisitions using different modalities such as functional Magnetic Resonance Imaging (MRI). Recently, deep learning frameworks were designed to predict a target medical modality from a source one (e.g., MRI from Computed Tomography (CT)). However, such methods which work well on images might fail when handling geometric brain data such as graphs (or connectomes). To the best of our knowledge, learning how to predict brain graph from a source graph based on geometric deep learning remains unexplored [1]. Given a set of isomorphic source and target brain graph (i.e., derived from the same parcellation brain template so their topology is similar), learning how to predict target brain graph from a source graph has two major challenges. The first one is that the source and target domains might have different distributions, which causes a domain fracture. The second challenge can be viewed as a limitation of existing image synthesis methods which address the domain fracture and multimodal data prediction independently. To address both limitations, we unprecedentedly propose a Symmetric Dual Adversarial Domain Alignment (SymDADA) framework for predicting target brain graph from a source graph. SymDADA aligns source and target domains by learning their shared embedding while alternating two regularization constraints: (i) adversarial regularization matching the distribution of the learned shared embedding with that of the source graphs using training and testing data, and (ii) adversarial regularization enforcing the embedded source distribution to match the distribution of the predicted target graphs using only the training samples. In this way, we are optimally adapting the source to the target space as we are jointly predicting the target graph when learning the graph embedding. Our proposed SymDADA framework outperformed its variants for predicting a target brain graph from a source graph in healthy and autistic subjects.