Fully Bayesian VIB-DeepSSM

• URL: http://arxiv.org/abs/2305.05797v2
• Date: Thu, 20 Jul 2023 16:36:32 GMT
• Title: Fully Bayesian VIB-DeepSSM
• Authors: Jadie Adams and Shireen Elhabian
• Abstract summary: Statistical shape modeling (SSM) enables quantitative analysis of anatomical shapes, informing clinical diagnosis. DeepSSM is an effective, principled framework for predicting probabilistic shapes of anatomy from images with aleatoric uncertainty quantification. We derive a fully Bayesian VIB formulation and demonstrate the efficacy of two scalable implementation approaches. Experiments on synthetic shapes and left atrium data demonstrate that the fully Bayesian VIB network predicts SSM from images with improved uncertainty reasoning without sacrificing accuracy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Statistical shape modeling (SSM) enables population-based quantitative analysis of anatomical shapes, informing clinical diagnosis. Deep learning approaches predict correspondence-based SSM directly from unsegmented 3D images but require calibrated uncertainty quantification, motivating Bayesian formulations. Variational information bottleneck DeepSSM (VIB-DeepSSM) is an effective, principled framework for predicting probabilistic shapes of anatomy from images with aleatoric uncertainty quantification. However, VIB is only half-Bayesian and lacks epistemic uncertainty inference. We derive a fully Bayesian VIB formulation and demonstrate the efficacy of two scalable implementation approaches: concrete dropout and batch ensemble. Additionally, we introduce a novel combination of the two that further enhances uncertainty calibration via multimodal marginalization. Experiments on synthetic shapes and left atrium data demonstrate that the fully Bayesian VIB network predicts SSM from images with improved uncertainty reasoning without sacrificing accuracy.

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