Semi-supervised Medical Image Segmentation

16 papers with code • 2 benchmarks • 2 datasets

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Use these libraries to find Semi-supervised Medical Image Segmentation models and implementations
5 papers
1,045

Most implemented papers

Mutual Consistency Learning for Semi-supervised Medical Image Segmentation

HiLab-git/SSL4MIS 21 Sep 2021

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

HiLab-git/SSL4MIS 2 Mar 2022

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 28 Feb 2019

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 16 Apr 2020

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 9 Sep 2020

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

YichiZhang98/DTML 8 Mar 2021

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 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

himashi92/Duo-SegNet 25 Aug 2021

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

HiLab-git/SSL4MIS 28 Sep 2021

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 17 Oct 2021

In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty.