Mutual consistency learning for semi-supervised medical image segmentation

Article Computer Science, Interdisciplinary Applications

SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation

Chenyu You et al.

Summary: Automated segmentation in medical image analysis is a challenging task. This work introduces a simple contrastive distillation framework, SimCVD, that improves the accuracy of image segmentation through unsupervised training and structural distillation. Experimental results on two datasets demonstrate the effectiveness of SimCVD.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2022)

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Proceedings Paper Computer Science, Interdisciplinary Applications

Momentum Contrastive Voxel-Wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation

Chenyu You et al.

Summary: This article introduces a novel method for contrastive voxel-wise representation learning in the context of medical image segmentation. By capturing 3D spatial context and rich anatomical information, this method effectively learns low-level and high-level features and inherits the benefits of hardness-aware property.

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT IV (2022)

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Proceedings Paper Computer Science, Interdisciplinary Applications

Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation

Yicheng Wu et al.

Summary: This paper proposes the SS-Net model for semi-supervised medical image segmentation tasks. The model is trained by exploring pixel-level smoothness and inter-class separation to address segmentation challenges in medical imaging. Experimental results demonstrate that the SS-Net model achieves state-of-the-art performance on public datasets.

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT V (2022)

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Article Computer Science, Artificial Intelligence

A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging

Zhaohan Xiong et al.

Summary: Automated segmentation of medical images, particularly for atrial fibrillation ablation treatment, is challenging yet crucial. This study utilized a large dataset and machine learning methods to optimize convolutional neural networks for achieving state-of-the-art left atrium segmentation, showing significant improvements over prior methods and setting a benchmark for future research in the field.

MEDICAL IMAGE ANALYSIS (2021)

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Article Computer Science, Artificial Intelligence

Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation

Zhedong Zheng et al.

Summary: This paper proposes an approach to rectify pseudo label learning in unsupervised semantic segmentation adaptation by explicitly estimating prediction uncertainty during training and incorporating uncertainty into the optimization objective. Through extensive experiments, the proposed method achieves significant improvements on three benchmarks.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2021)

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Review Computer Science, Artificial Intelligence

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Moloud Abdar et al.

Summary: Uncertainty quantification (UQ) methods are essential in reducing uncertainties in optimization and decision making processes. Bayesian approximation and ensemble learning techniques are widely used types of UQ methods.

INFORMATION FUSION (2021)

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Article Computer Science, Artificial Intelligence

Semi-Supervised Semantic Segmentation With High- and Low-Level Consistency

Sudhanshu Mittal et al.

Summary: The study introduces a semi-supervised semantic segmentation method that learns from limited labeled samples and unlabeled images, reducing low-level and high-level artifacts through adversarial training. It achieves significant improvement and new state-of-the-art results in semi-supervised learning.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

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Article Computer Science, Artificial Intelligence