2019/04/17 by Mhd Hasan Sarhan, Sarhan, Mhd Hasan, Abouzar Eslami +5
Computer Science · #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare
paper · pdf · doi:10.48550/arxiv.1904.08491
openalex publication_date 2019/04/17 · openalex created_date 2022/07/24 · openalex updated_date 2026/07/28
Learning Interpretable representation in medical applications is becoming\nessential for adopting data-driven models into clinical practice. It has been\nrecently shown that learning a disentangled feature representation is important\nfor a more compact and explainable representation of the data. In this paper,\nwe introduce a novel adversarial variational autoencoder with a total\ncorrelation constraint to enforce independence on the latent representation\nwhile preserving the reconstruction fidelity. Our proposed method is validated\non a publicly available dataset showing that the learned disentangled\nrepresentation is not only interpretable, but also superior to the\nstate-of-the-art methods. We report a relative improvement of 81.50% in terms\nof disentanglement, 11.60% in clustering, and 2% in supervised classification\nwith a few amounts of labeled data.\n