Deep variational clustering framework for self-labeling large-scale medical images

Soleymani, Farzin; Eslami, Mohammad; Elze, Tobias; Bischl, Bernd ; Rezaei, Mina; Išgum, Ivana und Colliot, Olivier (2022): Deep variational clustering framework for self-labeling large-scale medical images. Medical Imaging 2022: Image Processing, San Diego, California, United States, Online, 20–24 February 2022, 21-27 March 2022. Colliot, Olivier; Išgum, Ivana; Landman, Bennett A. und Loew, Murray H. (Hrsg.): In: Medical Imaging 2022: Image Processing : 20-24 February 2022, San Diego, California, United States : 21-27 March 2022, online, Proceedings of SPIE Bd. 12032 Bellingham, Washington, USA: SPIE. S. 9

Abstract

One of the most promising approaches for unsupervised learning is combining deep representation learning and deep clustering. Recent studies propose to simultaneously learn representation using deep neural networks and perform clustering by defining a clustering loss on top of embedded features. Unsupervised image clustering naturally requires good feature representations to capture the distribution of the data and subsequently differentiate data points from one another. Among existing deep learning models, the generative variational autoencoder explicitly learns data generating distribution in a latent space. We propose a Deep Variational Clustering (DVC) framework for unsupervised representation learning and clustering of large-scale medical images. DVC simultaneously learns the multivariate Gaussian posterior through the probabilistic convolutional encoder, and the likelihood distribution with the probabilistic convolutional decoder; and optimizes cluster labels assignment. Here, the learned multivariate Gaussian posterior captures the latent distribution of a large set of unlabeled images. Then, we perform unsupervised clustering on top of the variational latent space using a clustering loss. In this approach, the probabilistic decoder helps to prevent the distortion of data points in the latent space, and to preserve local structure of data generating distribution. The training process can be considered as a self-training process to refine the latent space and simultaneously optimizing cluster assignments iteratively. We evaluated our proposed framework on three public datasets that represented different medical imaging modalities. Our experimental results show that our proposed framework generalizes better across different datasets. It achieves compelling results on several medical imaging benchmarks. Thus, our approach offers potential advantages over conventional deep unsupervised learning in real-world applications. The source code of the method and of all the experiments are available publicly at: https://github.com/csfarzin/DVC

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• Ort: Bellingham, Washington, USA
• Bemerkung: 978-1-5106-4940-8 978-1-5106-4939-2 (ISBN der Printausgabe)
• Sprache: Englisch