Medical image segmentation is a fundamental and critical step in many clinical approaches. Semi-supervised learning has been widely applied to medical image segmentation tasks since it alleviates the heavy burden of acquiring expert-examined annotations and takes the advantage of unlabeled data which is much easier to acquire. Although consistency learning has been proven to be an effective approach by enforcing an invariance of predictions under different distributions, existing approaches cannot make full use of region-level shape constraint and boundary-level distance information from unlabeled data. In this paper, we propose a novel uncertainty-guided mutual consistency learning framework to effectively exploit unlabeled data by integra...
Semi-supervised learning for medical image segmentation is an important area of research for allevia...
We propose MisMatch, a novel consistency-driven semi-supervised segmentation framework which produc...
This paper concerns pseudo labelling in segmentation. Our contribution is fourfold. Firstly, we pres...
In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the un...
Medical image segmentation methods often rely on fully supervised approaches to achieve excellent pe...
This paper presents a simple yet effective two-stage framework for semi-supervised medical image seg...
Deep learning-based semi-supervised learning (SSL) algorithms have led to promising results in medic...
Background and Objective: Semi-supervised learning for medical image segmentation is an important ar...
Deep learning-based semi-supervised learning (SSL) algorithms are promising in reducing the cost of ...
The data-driven nature of deep learning (DL) models for semantic segmentation requires a large numbe...
Semi-supervised learning (SSL) is a promising machine learning paradigm to address the issue of labe...
Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labele...
Recent years have seen increasing use of supervised learning methods for segmentation tasks. However...
Recent years have seen an increasing use of supervised learning methods for segmentation tasks. Howe...
Classification and segmentation are crucial in medical image analysis as they enable accurate diagno...
Semi-supervised learning for medical image segmentation is an important area of research for allevia...
We propose MisMatch, a novel consistency-driven semi-supervised segmentation framework which produc...
This paper concerns pseudo labelling in segmentation. Our contribution is fourfold. Firstly, we pres...
In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the un...
Medical image segmentation methods often rely on fully supervised approaches to achieve excellent pe...
This paper presents a simple yet effective two-stage framework for semi-supervised medical image seg...
Deep learning-based semi-supervised learning (SSL) algorithms have led to promising results in medic...
Background and Objective: Semi-supervised learning for medical image segmentation is an important ar...
Deep learning-based semi-supervised learning (SSL) algorithms are promising in reducing the cost of ...
The data-driven nature of deep learning (DL) models for semantic segmentation requires a large numbe...
Semi-supervised learning (SSL) is a promising machine learning paradigm to address the issue of labe...
Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labele...
Recent years have seen increasing use of supervised learning methods for segmentation tasks. However...
Recent years have seen an increasing use of supervised learning methods for segmentation tasks. Howe...
Classification and segmentation are crucial in medical image analysis as they enable accurate diagno...
Semi-supervised learning for medical image segmentation is an important area of research for allevia...
We propose MisMatch, a novel consistency-driven semi-supervised segmentation framework which produc...
This paper concerns pseudo labelling in segmentation. Our contribution is fourfold. Firstly, we pres...