Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection
This work addresses pulmonary nodule detection for medical imaging, presenting an incremental improvement in uncertainty propagation within Bayesian deep learning architectures.
The paper tackled the problem of improving computer-aided detection of pulmonary nodules by propagating and fusing uncertainty information in a multi-stage Bayesian convolutional neural network, resulting in enhanced overall performance in prediction accuracy and model confidence.
Motivated by the problem of computer-aided detection (CAD) of pulmonary nodules, we introduce methods to propagate and fuse uncertainty information in a multi-stage Bayesian convolutional neural network (CNN) architecture. The question we seek to answer is "can we take advantage of the model uncertainty provided by one deep learning model to improve the performance of the subsequent deep learning models and ultimately of the overall performance in a multi-stage Bayesian deep learning architecture?". Our experiments show that propagating uncertainty through the pipeline enables us to improve the overall performance in terms of both final prediction accuracy and model confidence.