Semi-supervised Medical Image Segmentation
35 papers with code • 2 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.
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.
A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision
Gollini/NSCLC_Radiogenomics
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We further proposed a localization branch realized via an aggregation of high-level features in a deep decoder to predict locations of organ and lesion, which enriches student segmentor with precise localization information.
