Variational Inference and Bayesian CNNs for Uncertainty Estimation in Multi-Factorial Bone Age Prediction
Additionally to the extensive use in clinical medicine, biological age (BA) in legal medicine is used to assess unknown chronological age (CA) in applications where identification documents are not available. Automatic methods for age estimation proposed in the literature are predicting point estimates, which can be misleading without the quantification of predictive uncertainty. In our multi-factorial age estimation method from MRI data, we used the Variational Inference approach to estimate the uncertainty of a Bayesian CNN model. Distinguishing model uncertainty from data uncertainty, we interpreted data uncertainty as biological variation, i.e. the range of possible CA of subjects having the same BA.
Code (0)
등록된 구현이 없습니다.
Tasks
Age EstimationVariational InferenceSimilar Papers 제목 키워드 기반
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics
We propose VarFA, a variational inference factor analysis framework that extends existing factor analysis models for educational data mining to efficiently output uncertainty estimation in the model's estimated factors. …
Bayesian InferenceVariational InferenceUncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference
We introduce a novel uncertainty estimation for classification tasks for Bayesian convolutional neural networks with variational inference. By normalizing the output of a Softplus function in the final layer, we estimate…
Bayesian InferenceGeneral ClassificationVariational InferenceDeep Bayesian Structure Networks
Bayesian neural networks (BNNs) introduce uncertainty estimation to deep networks by performing Bayesian inference on network weights. However, such models bring the challenges of inference, and further BNNs with weight …
Bayesian InferenceNeural Architecture SearchVariational InferenceStatistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection
Variational inference has recently emerged as a popular alternative to the classical Markov chain Monte Carlo (MCMC) in large-scale Bayesian inference. The core idea is to trade statistical accuracy for computational eff…
Bayesian InferenceComputational EfficiencyModel SelectionStochastic Optimization+2Variational Bayesian Last Layers
We introduce a deterministic variational formulation for training Bayesian last layer neural networks. This yields a sampling-free, single-pass model and loss that effectively improves uncertainty estimation. Our variati…
Out-of-Distribution DetectionVariational Inference