Semi-supervised Medical Image Segmentation
16 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.
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.
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation
xmengli999/tcsm
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In this paper, we present a novel semi-supervised method for medical image segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.
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.
Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation
The success of deep learning methods in medical image segmentation tasks usually requires a large amount of labeled data.
Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels
jizongFox/Self-paced-Contrastive-Learning
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NeurIPS 2021
Pre-training a recognition model with contrastive learning on a large dataset of unlabeled data has shown great potential to boost the performance of a downstream task, e. g., image classification.
Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation
Segmentation of images is a long-standing challenge in medical AI.
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation
Observing this, we ask an unexplored but interesting question: can we exploit the unlabeled data via explicit real label supervision for semi-supervised training?
Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation
koncle/coranet
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In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty.
