Semi-supervised Medical Image Segmentation
46 papers with code • 5 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Semi-supervised Medical Image Segmentation
Libraries
Use these libraries to find Semi-supervised Medical Image Segmentation models and implementations
ACDC
MM-WHS 2017
Most implemented papers
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.
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.
Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation
huiimin5/aua
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A stage-adaptive contrastive learning method is proposed, containing a boundary-aware contrastive loss that takes advantage of the labeled images in the first stage, as well as a prototype-aware contrastive loss to optimize both labeled and pseudo labeled images in the second stage.
Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer
Notably, this work may be the first attempt to combine CNN and transformer for semi-supervised medical image segmentation and achieve promising results on a public benchmark.
An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation
The scarcity of pixel-level annotation is a prevalent problem in medical image segmentation tasks.
Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Segmentation
Semi-supervised learning (SSL), which aims at leveraging a few labeled images and a large number of unlabeled images for network training, is beneficial for relieving the burden of data annotation in medical image segmentation.
