On-the-Fly Test-time Adaptation for Medical Image Segmentation

Understand

One major problem in deep learning-based solutions for medical imaging is the drop in performance when a model is tested on a data distribution different from the one that it is trained on.

• Adapting the source model to target data distribution at test-time is an efficient solution for the data-shift problem.
• Previous methods solve this by adapting the model to target distribution by using techniques like entropy minimization or regularization.
• In these methods, the models are still updated by back-propagation using an unsupervised loss on complete test data distribution.