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GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference

Ghorbani M, Bahrami M, Kazi A, Baghshah MS, Rabiee HR, Navab N (2021)

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Publication Type: Conference contribution

Publication year: 2021

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 12905 LNCS

Pages Range: 709-718

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Virtual, Online

ISBN: 9783030872397

DOI: 10.1007/978-3-030-87240-3_68

Abstract

The increased amount of multi-modal medical data has opened the opportunities to simultaneously process various modalities such as imaging and non-imaging data to gain a comprehensive insight into the disease prediction domain. Recent studies using Graph Convolutional Networks (GCNs) provide novel semi-supervised approaches for integrating heterogeneous modalities while investigating the patients’ associations for disease prediction. However, when the meta-data used for graph construction is not available at inference time (e.g., coming from a distinct population), the conventional methods exhibit poor performance. To address this issue, we propose a novel semi-supervised approach named GKD based on the knowledge distillation. We train a teacher component that employs the label-propagation algorithm besides a deep neural network to benefit from the graph and non-graph modalities only in the training phase. The teacher component embeds all the available information into the soft pseudo-labels. The soft pseudo-labels are then used to train a deep student network for disease prediction of unseen test data for which the graph modality is unavailable. We perform our experiments on two public datasets for diagnosing Autism spectrum disorder, and Alzheimer’s disease, along with a thorough analysis on synthetic multi-modal datasets. According to these experiments, GKD outperforms the previous graph-based deep learning methods in terms of accuracy, AUC, and Macro F1.

Involved external institutions

How to cite

APA:

Ghorbani, M., Bahrami, M., Kazi, A., Baghshah, M.S., Rabiee, H.R., & Navab, N. (2021). GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference. In Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 709-718). Virtual, Online: Springer Science and Business Media Deutschland GmbH.

MLA:

Ghorbani, Mahsa, et al. "GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference." Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert, Springer Science and Business Media Deutschland GmbH, 2021. 709-718.

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