  We propose an adaptive training scheme for unsupervised medical image
registration. Existing methods rely on image reconstruction as the primary
supervision signal. However, nuisance variables (e.g. noise and covisibility),
violation of the Lambertian assumption in physical waves (e.g. ultrasound), and
inconsistent image acquisition can all cause a loss of correspondence between
medical images. As the unsupervised learning scheme relies on intensity
constancy between images to establish correspondence for reconstruction, this
introduces spurious error residuals that are not modeled by the typical
training objective. To mitigate this, we propose an adaptive framework that
re-weights the error residuals with a correspondence scoring map during
training, preventing the parametric displacement estimator from drifting away
due to noisy gradients, which leads to performance degradation. To illustrate
the versatility and effectiveness of our method, we tested our framework on
three representative registration architectures across three medical image
datasets along with other baselines. Our adaptive framework consistently
outperforms other methods both quantitatively and qualitatively. Paired t-tests
show that our improvements are statistically significant. Code available at:
\url{https://voldemort108x.github.io/AdaCS/}.
