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Found 11 papers, 0 papers with code
An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentation
Additionally, the evidential fusion branch capitalizes on the complementary attributes of the first two branches and leverages an evidence-based Dempster-Shafer fusion strategy, supervised by more reliable and accurate pseudo-labels of unlabeled data.
Multi Task Consistency Guided Source-Free Test-Time Domain Adaptation Medical Image Segmentation
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.
Topology-inspired Cross-domain Network for Developmental Cervical Stenosis Quantification
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.
Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation
This paper proposes a cross-supervised learning framework based on dual classifiers (DC-Net), including an evidential classifier and a vanilla classifier.
Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation
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.
Position-Aware Relation Learning for RGB-Thermal Salient Object Detection
FRDF utilizes the directional information between object pixels to effectively enhance the intra-class compactness of salient regions.
Mutual- and Self- Prototype Alignment for Semi-supervised Medical Image Segmentation
In specific, mutual-prototype alignment enhances the information interaction between labeled and unlabeled data.
PixelGame: Infrared small target segmentation as a Nash equilibrium
FNs-player and FPs-player are designed with different strategies: One is to minimize FNs and the other is to minimize FPs.
Collaborative Boundary-aware Context Encoding Networks for Error Map Prediction
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.
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation
3D image segmentation is one of the most important and ubiquitous problems in medical image processing.
An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms
We train the deep encoder-decoder for landmark detection, and combine global landmark configuration with local high-resolution feature responses.
