1 research outputs found
Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networks
Unsupervised image transfer enables intra- and inter-modality image
translation in applications where a large amount of paired training data is not
abundant. To ensure a structure-preserving mapping from the input to the target
domain, existing methods for unpaired image transfer are commonly based on
cycle-consistency, causing additional computational resources and instability
due to the learning of an inverse mapping. This paper presents a novel method
for uni-directional domain mapping that does not rely on any paired training
data. A proper transfer is achieved by using a GAN architecture and a novel
generator loss based on patch invariance. To be more specific, the generator
outputs are evaluated and compared at different scales, also leading to an
increased focus on high-frequency details as well as an implicit data
augmentation. This novel patch loss also offers the possibility to accurately
predict aleatoric uncertainty by modeling an input-dependent scale map for the
patch residuals. The proposed method is comprehensively evaluated on three
well-established medical databases. As compared to four state-of-the-art
methods, we observe significantly higher accuracy on these datasets, indicating
great potential of the proposed method for unpaired image transfer with
uncertainty taken into account. Implementation of the proposed framework is
released here:
\url{https://github.com/anger-man/unsupervised-image-transfer-and-uq}.Comment: Accepted to ECCV 2022 Workshop on Uncertainty Quantification for
Computer Vision (UNCV 2022