# Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection

arXiv: https://arxiv.org/abs/1712.00497 · HTML: https://mlsanity.com/paper/1712.00497/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2017.

## Built on by (most cited first, 11 of 11)

- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis](https://mlsanity.com/paper/2210.03736/) (2022)
- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [False Negative Reduction in Video Instance Segmentation using Uncertainty Estimates](https://mlsanity.com/paper/2106.14474/) (2021)
- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)
- [Using uncertainty estimation to reduce false positives in liver lesion detection](https://mlsanity.com/paper/2101.04386/) (2021)
- [Propagation and Attribution of Uncertainty in Medical Imaging Pipelines](https://mlsanity.com/paper/2309.16831/) (2023)
- [Uncertainty categories in medical image segmentation: a study of source-related diversity](https://mlsanity.com/paper/2203.00238/) (2022)
- [False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation](https://mlsanity.com/paper/2110.15681/) (2021)
- [Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation](https://mlsanity.com/paper/2211.00303/) (2022)
- [Decomposition-Based Modular Conformal Prediction for Two-Stage Modeling](https://mlsanity.com/paper/2510.04406/) (2025)

## Abstract (arXiv)

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.