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Inter-domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning

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Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health (DART 2021, FAIR 2021)

Abstract

Accurate and automated super-resolution image synthesis is highly desired since it has the great potential to circumvent the need for acquiring high-cost medical scans and a time-consuming preprocessing pipeline of neuroimaging data. However, existing deep learning frameworks are solely designed to predict high-resolution (HR) image from a low-resolution (LR) one, which limits their generalization ability to brain graphs (i.e., connectomes). A small body of works has focused on superresolving brain graphs where the goal is to predict a HR graph from a single LR graph. Although promising, existing works mainly focus on superresolving graphs belonging to the same domain (e.g., functional), overlooking the domain fracture existing between multimodal brain data distributions (e.g., morphological and structural). To this aim, we propose a novel inter-domain adaptation framework namely, Learn to SuperResolve Brain Graphs with Knowledge Distillation Network (L2S-KDnet), which adopts a teacher-student paradigm to superresolve brain graphs. Our teacher network is a graph encoder-decoder that firstly learns the LR brain graph embeddings, and secondly learns how to align the resulting latent representations to the HR ground truth data distribution using an adversarial regularization. Ultimately, it decodes the HR graphs from the aligned embeddings. Next, our student network learns the knowledge of the aligned brain graphs as well as the topological structure of the predicted HR graphs transferred from the teacher. We further leverage the decoder of the teacher to optimize the student network. In such a way, we are not only bringing the learned embeddings from both networks closer to each other but also their predicted HR graphs. L2S-KDnet presents the first TS architecture tailored for brain graph super-resolution synthesis that is based on inter-domain alignment. Our experimental results demonstrate substantial performance gains over benchmark methods. Our code is available at https://github.com/basiralab/L2S-KDnet.

B. Demir and A. Bessadok—Co-first authors.

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Acknowledgements

This work was funded by generous grants from the European H2020 Marie Sklodowska-Curie action (grant no. 101003403, http://basira-lab.com/normnets/) to I.R. and the Scientific and Technological Research Council of Turkey to I.R. under the TUBITAK 2232 Fellowship for Outstanding Researchers (no. 118C288, http://basira-lab.com/reprime/). However, all scientific contributions made in this project are owned and approved solely by the authors. A.B is supported by the same TUBITAK 2232 Fellowship.

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Correspondence to Islem Rekik .

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Demir, B., Bessadok, A., Rekik, I. (2021). Inter-domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning. In: Albarqouni, S., et al. Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health. DART FAIR 2021 2021. Lecture Notes in Computer Science(), vol 12968. Springer, Cham. https://doi.org/10.1007/978-3-030-87722-4_19

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  • DOI: https://doi.org/10.1007/978-3-030-87722-4_19

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