Analysis of Generative Shape Modeling Approaches: Latent Space Properties and Interpretability

Publikation

Hristina Uzunova, Jesse Kruse, Paul Kaftan, Matthias Wilms, Nils D Forkert, Heinz Handels, Jan Ehrhardt

In: Bildverarbeitung für die Medizin 2021: Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021. Workshop Bildverarbeitung für die Medizin (BVM-2021) March 7-9 Regensburg Germany Seiten 344-349 Springer 2021.

Abstrakt

Generative shape models are crucial for many medical image analysis tasks. In previous studies, it has been shown that conventional methods like PCA-based statistical shape models (SSMs) and their extensions are thought to be robust in terms of generalization ability but have rather poor specificity. On the contrary, deep learning approaches like autoencoders, require large training set sizes, but are comparably specific. In this work, we comprehensively compare different classical and deep learning-based generative shape modeling approaches and demonstrate their limitations and advantages. Experiments on a publicly available 2D chest X-ray data set show that the deep learning methods achieve better specificity and similar generalization abilities for large training set sizes. Furthermore, an extensive analysis of the different methods, gives an insight on their latent space representations.

Analysis of Generative Shape Modeling Approaches: Latent Space Properties and Interpretability

Abstrakt

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