Concatenate, Fine-tuning, Re-training: SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation
Proposing a three-stage framework, CFR, leveraging SAM for semi-supervised 3D medical image segmentation, achieving significant improvements in annotation efficiency and performance.
Uncertainty-aware Evidential Fusion-based Semi-supervised Learning for Accurate Medical Image Segmentation
The core message of this article is to propose a novel uncertainty-aware evidential fusion-based learning framework for semi-supervised medical image segmentation, which integrates evidential predictive results from mixed and original samples to reallocate the confidence degree and uncertainty measure of each voxel, and further designs a voxel-level asymptotic learning strategy to guide the model to focus on hard-to-learn features.
An Evidential Tri-Branch Consistency Learning Framework for Effective Semi-supervised Medical Image Segmentation
The core message of this article is to propose an Evidential Tri-Branch Consistency learning framework (ETC-Net) that employs three branches - an evidential conservative branch, an evidential progressive branch, and an evidential fusion branch - to effectively leverage both labeled and unlabeled data for semi-supervised medical image segmentation. The framework integrates evidential learning, uncertainty guidance, and evidential fusion to address critical issues such as prediction disagreement and label-noise suppression in cross-supervised training.
Diagonal Hierarchical Consistency Learning for Robust Semi-supervised Medical Image Segmentation
A novel semi-supervised medical image segmentation framework, DiHC-Net, that leverages diagonal hierarchical consistency learning between multiple diversified sub-models to effectively utilize scarce labeled data and abundant unlabeled data.
Enhancing Semi-Supervised Medical Image Segmentation with Perturbation Strategies and Knowledge Distillation
CrossMatch, a novel framework that integrates knowledge distillation with dual perturbation strategies - image-level and feature-level - to improve the model's learning from both labeled and unlabeled data, significantly surpassing other state-of-the-art techniques in standard benchmarks.
Adaptive Bidirectional Displacement: A Novel Approach for Enhancing Semi-Supervised Medical Image Segmentation
The proposed Adaptive Bidirectional Displacement (ABD) approach mitigates the constraints of mixed perturbations on consistency learning, thereby enhancing the upper limit of consistency learning for semi-supervised medical image segmentation.
Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation
A novel semi-supervised learning framework, termed Progressive Mean Teachers (PMT), is proposed to generate high-fidelity pseudo labels by learning robust and diverse features in the training process.
Compound Multi-Attention Transformer and Lagrange Duality Consistency Loss for Semi-Supervised Medical Image Segmentation
The paper proposes a novel Compound Multi-Attention Transformer (CMAformer) architecture that synergizes the strengths of ResUNet and Transformer models, and introduces a Lagrange Duality Consistency (LDC) Loss for semi-supervised learning to address the long-tail problem in medical image analysis.
Manifold-Aware Local Feature Modeling Network (MANet) for Enhanced Medical Image Segmentation with Limited Labeled Data
Integrating manifold information into semi-supervised learning methods significantly improves the boundary accuracy of medical image segmentation, especially when limited labeled data is available.
Attention-Guided Consistency Regularization for Semi-Supervised Medical Image Segmentation Using AIGCMatch
This research paper introduces AIGCMatch, a novel semi-supervised learning framework that leverages attention-guided perturbations at both the image and feature levels to improve the accuracy and efficiency of medical image segmentation models, particularly in scenarios with limited labeled data.
