Reconstructing and analyzing the invariances of low-dose CT image denoising networks.

ABSTRACT:

Background

Deep learning-based methods led to significant advancements in many areas of medical imaging, most of which are concerned with the reduction of artifacts caused by motion, scatter, or noise. However, with most neural networks being black boxes, they remain notoriously difficult to interpret, hindering their clinical implementation. In particular, it has been shown that networks exhibit invariances w.r.t. input features, that is, they learn to ignore certain information in the input data.

Purpose

To improve the interpretability of deep learning-based low-dose CT image denoising networks.

Methods

We learn a complete data representation of low-dose input images using a conditional variational autoencoder (cVAE). In this representation, invariances of any given

SUBMITTER: Eulig E 

PROVIDER: S-EPMC11700010 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature