Semi-supervised Medical Image Segmentation

insight - Semi-supervised Medical Image Segmentation

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.

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