# Uncertainty Estimation for Trust Attribution to Speed-of-Sound Reconstruction with Variational Networks

arXiv: https://arxiv.org/abs/2504.11307 · HTML: https://mlsanity.com/paper/2504.11307/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 15 of 15)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Deep Convolutional Neural Network for Inverse Problems in Imaging](https://mlsanity.com/paper/1611.03679/) (2016)
- [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019)
- [Learning a Variational Network for Reconstruction of Accelerated MRI Data](https://mlsanity.com/paper/1704.00447/) (2017)
- [Image reconstruction by domain transform manifold learning](https://mlsanity.com/paper/1704.08841/) (2017)
- [Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction](https://mlsanity.com/paper/2102.06665/) (2021)
- [So you think you can DAS? A viewpoint on delay-and-sum beamforming](https://mlsanity.com/paper/2007.11960/) (2020)
- [A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound](https://mlsanity.com/paper/1810.00322/) (2018)
- [Uncertainty Quantification for Deep Unrolling-Based Computational Imaging](https://mlsanity.com/paper/2207.00698/) (2022)
- [Deep Variational Networks with Exponential Weighting for Learning Computed Tomography](https://mlsanity.com/paper/1906.05528/) (2019)
- [Training Variational Networks with Multi-Domain Simulations: Speed-of-Sound Image Reconstruction](https://mlsanity.com/paper/2006.14395/) (2020)
- [Speed-of-Sound Imaging using Diverging Waves](https://mlsanity.com/paper/1910.05935/) (2019)
- [Analytical Estimation of Beamforming Speed-of-Sound Using Transmission Geometry](https://mlsanity.com/paper/2211.11553/) (2022)
- [Robust Imaging of Speed-of-Sound Using Virtual Source Transmission](https://mlsanity.com/paper/2303.11262/) (2023)
- [Learning the Imaging Model of Speed-of-Sound Reconstruction via a Convolutional Formulation](https://mlsanity.com/paper/2309.00453/) (2023)

## Abstract (arXiv)

Speed-of-sound (SoS) is a biomechanical characteristic of tissue, and its imaging can provide a promising biomarker for diagnosis. Reconstructing SoS images from ultrasound acquisitions can be cast as a limited-angle computed-tomography problem, with Variational Networks being a promising model-based deep learning solution. Some acquired data frames may, however, get corrupted by noise due to, e.g., motion, lack of contact, and acoustic shadows, which in turn negatively affects the resulting SoS reconstructions. We propose to use the uncertainty in SoS reconstructions to attribute trust to each individual acquired frame. Given multiple acquisitions, we then use an uncertainty based automatic selection among these retrospectively, to improve diagnostic decisions. We investigate uncertainty estimation based on Monte Carlo Dropout and Bayesian Variational Inference. We assess our automatic frame selection method for differential diagnosis of breast cancer, distinguishing between benign fibroadenoma and malignant carcinoma. We evaluate 21 lesions classified as BI-RADS~4, which represents suspicious cases for probable malignancy. The most trustworthy frame among four acquisitions of each lesion was identified using uncertainty based criteria. Selecting a frame informed by uncertainty achieved an area under curve of 76% and 80% for Monte Carlo Dropout and Bayesian Variational Inference, respectively, superior to any uncertainty-uninformed baselines with the best one achieving 64%. A novel use of uncertainty estimation is proposed for selecting one of multiple data acquisitions for further processing and decision making.