Semi-supervised Medical Image Segmentation
48 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
Translation Consistent Semi-supervised Segmentation for 3D Medical Images
yyliu01/tracoco
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The most successful SSL approaches are based on consistency learning that minimises the distance between model responses obtained from perturbed views of the unlabelled data.
Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation
In this work, we present ACTION, an Anatomical-aware ConTrastive dIstillatiON framework, for semi-supervised medical image segmentation.
Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation
Secondly, in fact, they are only partially based on Bayesian deep learning, as their overall architectures are not designed under the Bayesian framework.
Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation
Secondly, we propose a semi-supervised medical image segmentation method purely based on the original pseudo labelling, namely SegPL.
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
LiheYoung/UniMatch
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CVPR 2023
In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch from semi-supervised classification, where the prediction of a weakly perturbed image serves as supervision for its strongly perturbed version.
IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation
To this end, we extend the concept of metric learning to the segmentation task, using a dense (dis)similarity learning for pre-training a deep encoder network, and employing a semi-supervised paradigm to fine-tune for the downstream task.
MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery
deepmed-lab-ecnu/magicnet
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CVPR 2023
Our strategy encourages unlabeled images to learn organ semantics in relative locations from the labeled images (cross-branch) and enhances the learning ability for small organs (within-branch).
MCF: Mutual Correction Framework for Semi-Supervised Medical Image Segmentation
wyc-321/mcf
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CVPR 2023
Inspired by the plain contrast idea, MCF introduces two different subnets to explore and utilize the discrepancies between subnets to correct cognitive bias of the model.
CauSSL: Causality-inspired Semi-supervised Learning for Medical Image Segmentation
juzhengmiao/caussl
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ICCV 2023
Specifically, we first point out the importance of algorithmic independence between two networks or branches in SSL, which is often overlooked in the literature.
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
senyh/ucmt
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Combining the strengths of UMIX with CMT, UCMT can retain model disagreement and enhance the quality of pseudo labels for the co-training segmentation.
