  Test-time adaptation (TTA) has increasingly been an important topic to
efficiently tackle the cross-domain distribution shift at test time for medical
images from different institutions. Previous TTA methods have a common
limitation of using a fixed learning rate for all the test samples. Such a
practice would be sub-optimal for TTA, because test data may arrive
sequentially therefore the scale of distribution shift would change frequently.
To address this problem, we propose a novel dynamic learning rate adjustment
method for test-time adaptation, called DLTTA, which dynamically modulates the
amount of weights update for each test image to account for the differences in
their distribution shift. Specifically, our DLTTA is equipped with a memory
bank based estimation scheme to effectively measure the discrepancy of a given
test sample. Based on this estimated discrepancy, a dynamic learning rate
adjustment strategy is then developed to achieve a suitable degree of
adaptation for each test sample. The effectiveness and general applicability of
our DLTTA is extensively demonstrated on three tasks including retinal optical
coherence tomography (OCT) segmentation, histopathological image
classification, and prostate 3D MRI segmentation. Our method achieves effective
and fast test-time adaptation with consistent performance improvement over
current state-of-the-art test-time adaptation methods. Code is available at:
https://github.com/med-air/DLTTA.
