Test Time Adaptation involves making adjustments to a model’s parameters or decision-making processes based on the characteristics of the data it encounters at test time. Test time adaptation can be employed to improve the model’s performance in situations that may differ from the training data, ensuring better generalization and robustness.


Improving Test-Time Adaptation For Histopathology Image Segmentation: Gradient-To-Parameter Ratio Guided Feature Alignment

In the field of histopathology, computer-aided systems face significant challenges due to diverse domain shifts. They include variations in tissue source organ, preparation and scanningprotocols. These domain shifts can significantly impact algorithms’ performance in histopathology tasks, such as cancer segmentation. In this paper, we address this problem byproposing a new multi-task extension of test-time adaptation (TTA) for simultaneous semantic, and instance segmentation of nuclei. First, to mitigate domain shifts during testing, weuse a feature alignment TTA method, through which we adapt the feature vectors of the target data based on the feature vectors’ statistics derived from the source data. Second, the ratioof Gradient norm to Parameter norm (G2P) is proposed to guide the feature alignment procedure. Our approach requires a pre-trained model on the source data, without requiringaccess to the source dataset during TTA. This is particularly crucial in medical applications where access to training data may be restricted due to privacy concerns or patient consent. Through experimental validation, we demonstrate that the proposed method consistently yields competitive results when applied to out-of-distribution data across multiple datasets.