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.
However, the vertebral visualization features often lead to abnormal topological structures during keypoint localization, including keypoint distortion with edges and weakly connected structures, which cannot be fully suppressed in either the coordinate or image domain alone.
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.
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.
3D image segmentation is one of the most important and ubiquitous problems in medical image processing.
We train the deep encoder-decoder for landmark detection, and combine global landmark configuration with local high-resolution feature responses.