Learning interpretable disentangled representations using adversarial VAEs
Sarhan MH, Eslami A, Navab N, Albarqouni S (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer
Book Volume: 11795 LNCS
Pages Range: 37-44
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Shenzhen, CHN
ISBN: 9783030333904
DOI: 10.1007/978-3-030-33391-1_5
Abstract
Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice. It has been recently shown that learning a disentangled feature representation is important for a more compact and explainable representation of the data. In this paper, we introduce a novel adversarial variational autoencoder with a total correlation constraint to enforce independence on the latent representation while preserving the reconstruction fidelity. Our proposed method is validated on a publicly available dataset showing that the learned disentangled representation is not only interpretable, but also superior to the state-of-the-art methods. We report a relative improvement of 81.50 % in terms of disentanglement, 11.60 % in clustering, and 2 % in supervised classification with a few amount of labeled data.
Involved external institutions
Germany (DE)How to cite
APA:
Sarhan, M.H., Eslami, A., Navab, N., & Albarqouni, S. (2019). Learning interpretable disentangled representations using adversarial VAEs. In Qian Wang, Fausto Milletari, Nicola Rieke, Hien V. Nguyen, Badri Roysam, Shadi Albarqouni, M. Jorge Cardoso, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Steve Jiang, Kevin Zhou, Khoa Luu, Ngan Le (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 37-44). Shenzhen, CHN: Springer.
MLA:
Sarhan, Mhd Hasan, et al. "Learning interpretable disentangled representations using adversarial VAEs." Proceedings of the 1st MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the 1st International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer- Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Qian Wang, Fausto Milletari, Nicola Rieke, Hien V. Nguyen, Badri Roysam, Shadi Albarqouni, M. Jorge Cardoso, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Steve Jiang, Kevin Zhou, Khoa Luu, Ngan Le, Springer, 2019. 37-44.
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