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| Home > Publications database > Reconstructing and analyzing the invariances of low-dose CT image denoising networks. |
| Home > Publications database > Reconstructing and analyzing the invariances of low-dose CT image denoising networks. |
| Journal Article | DKFZ-2024-01967 |
; ; ; ;
2025
AAPM
College Park, Md.
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Please use a persistent id in citations: doi:10.1002/mp.17413
Abstract: 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.To improve the interpretability of deep learning-based low-dose CT image denoising networks.We learn a complete data representation of low-dose input images using a conditional variational autoencoder (cVAE). In this representation, invariances of any given denoising network are then disentangled from the information it is not invariant to using a conditional invertible neural network (cINN). At test time, image-space invariances are generated by applying the inverse of the cINN and subsequent decoding using the cVAE. We propose two methods to analyze sampled invariances and to find those that correspond to alterations of anatomical structures.The proposed method is applied to four popular deep learning-based low-dose CT image denoising networks. We find that the networks are not only invariant to noise amplitude and realizations, but also to anatomical structures.The proposed method is capable of reconstructing and analyzing invariances of deep learning-based low-dose CT image denoising networks. This is an important step toward interpreting deep learning-based methods for medical imaging, which is essential for their clinical implementation.
Keyword(s): computed tomography ; deep learning ; explainability ; invariances ; low‐dose ; robustness
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