Uncertainty Estimation for Trust Attribution to Speed-of-Sound Reconstruction with Variational Networks
Journal:
arXiv
Published Date:
Apr 15, 2025
Abstract
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.
