Semi-supervised Medical Image Segmentation
16 papers with code • 2 benchmarks • 2 datasets
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In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation.
The pixel-level smoothness forces the model to generate invariant results under adversarial perturbations.
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
To address this problem, we propose a generic semi-supervised learning framework for image segmentation based on a deep convolutional neural network (DCNN).
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.
The success of deep learning methods in medical image segmentation tasks usually requires a large amount of labeled data.
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.
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?
In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty.