Improving Artifact Robustness for CT Deep Learning Models Without Labeled Artifact Images via Domain Adaptation

TL;DR

Empirical evidence is provided that domain adaptation can effectively address distribution shift in medical imaging without requiring expensive expert labeling of new artifact distributions, suggesting promise for deployment in clinical settings where novel artifacts may emerge.

Abstract

Deep learning models which perform well on images from their training distribution can degrade substantially when applied to new distributions. If a CT scanner introduces a new artifact not present in the training labels, the model may misclassify the images. Although modern CT scanners include design features which mitigate these artifacts, unanticipated or difficult-to-mitigate artifacts can still appear in practice. The direct solution of labeling images from this new distribution can be costly. As a more accessible alternative, this study evaluates domain adaptation as an approach for training models that maintain classification performance despite new artifacts, even without corresponding labels. We simulate ring artifacts from detector gain error in sinogram space and evaluate domain adversarial neural networks (DANN) against baseline and augmentation-based approaches on the OrganAMNIST abdominal CT dataset. Our results demonstrate that baseline models trained only on clean images fail to generalize to images with ring artifacts, and traditional augmentation with other distortion types provides no improvement on unseen artifact domains. In contrast, the DANN approach successfully maintains high classification accuracy on ring artifact images using only unlabeled artifact data during training, demonstrating the viability of domain adaptation for artifact robustness. The domain-adapted model achieved classification performance on ring artifact test data comparable to models explicitly trained with labeled artifact images, while also showing unexpected generalization to uniform noise. These findings provide empirical evidence that domain adaptation can effectively address distribution shift in medical imaging without requiring expensive expert labeling of new artifact distributions, suggesting promise for deployment in clinical settings where novel artifacts may emerge.

Figures (12)

• Figure 1: Samples from our distorted OrganAMNIST dataset exhibiting no distortion, uniform noise, and rotation by 90°.
• Figure 2: Reconstruction error example (first image is $R$, second image is $O$). A difference is visible between the original image and the result after forward projection and backprojection.
• Figure 3: Unintended reconstruction error mixed with intended simulated ring artifact (first image is $R_{distorted}$). For ease of visualization, we simulate 5 adjacent detector channels with gain error -10%.
• Figure 4: Result of our proposed approach (first image is $O_{distorted}$). For ease of visualization, we simulate 5 adjacent detector channels with gain error -10%.
• Figure 5: Samples from our distorted OrganAMNIST dataset exhibiting our simulated ring artifact with up to 10% gain error in all simulated detectors.