To improve the performance of test-time domain adaptation, we propose a multi task consistency guided source-free test-time domain adaptation medical image segmentation method which ensures the consistency of the local boundary predictions and the global prototype representation.
This paper proposes a cross-supervised learning framework based on dual classifiers (DC-Net), including an evidential classifier and a vanilla classifier.
To address these issues, we propose a self-aware and cross-sample prototypical learning method (SCP-Net) to enhance the diversity of prediction in consistency learning by utilizing a broader range of semantic information derived from multiple inputs.
FRDF utilizes the directional information between object pixels to effectively enhance the intra-class compactness of salient regions.
The Contrastive Language-Image Pre-training (CLIP) Model is a recently proposed large-scale pre-train model which attracts increasing attention in the computer vision community.
In specific, mutual-prototype alignment enhances the information interaction between labeled and unlabeled data.
FNs-player and FPs-player are designed with different strategies: One is to minimize FNs and the other is to minimize FPs.
Further, we propose a context encoding module to utilize the global predictor from the error map to enhance the feature representation and regularize the networks.
To address this problem, a novel reweighted Laplace prior based hyperspectral compressive sensing method is proposed in this study.