Semi-supervised Medical Image Segmentation
58 papers with code • 7 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Semi-supervised Medical Image Segmentation
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Libraries
Use these libraries to find Semi-supervised Medical Image Segmentation models and implementations
ACDC
MM-WHS 2017
Most implemented papers
Mutual Consistency Learning for Semi-supervised Medical Image Segmentation
In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation.
MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels
The state-of-the-art SSL methods in image classification utilise consistency regularisation to learn unlabelled predictions which are invariant to input level perturbations.
Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation
The pixel-level smoothness forces the model to generate invariant results under adversarial perturbations.
When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation
A topological exploration of all alternative supervision modes with CNN and ViT are detailed validated, demonstrating the most promising performance and specific setting of our method on semi-supervised medical image segmentation tasks.
Pseudo-Label Guided Contrastive Learning for Semi-Supervised Medical Image Segmentation
HiLab-git/SSL4MIS
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CVPR 2023
Although recent works in semi-supervised learning (SemiSL) have accomplished significant success in natural image segmentation, the task of learning discriminative representations from limited annotations has been an open problem in medical images.
Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation
Semi-supervised medical image segmentation has attracted much attention in recent years because of the high cost of medical image annotations.
ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast
In this work, we present ACTION++, an improved contrastive learning framework with adaptive anatomical contrast for semi-supervised medical segmentation.
Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation
HiLab-git/SSL4MIS
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CVPR 2023
In semi-supervised medical image segmentation, there exist empirical mismatch problems between labeled and unlabeled data distribution.
A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation
ruizhe-l/semi-segmentation
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To address this problem, we propose a generic semi-supervised learning framework for image segmentation based on a deep convolutional neural network (DCNN).
Semi-supervised Medical Image Segmentation through Dual-task Consistency
HiLab-git/DTC
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Concretely, we use a dual-task deep network that jointly predicts a pixel-wise segmentation map and a geometry-aware level set representation of the target.
