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Unsupervised Machine Learning

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Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology.

Radiation oncology (London, England)
PURPOSE: Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensional (3D) convolutional U-Net ba...

Active Dynamic Weighting for multi-domain adaptation.

Neural networks : the official journal of the International Neural Network Society
Multi-source unsupervised domain adaptation aims to transfer knowledge from multiple labeled source domains to an unlabeled target domain. Existing methods either seek a mixture of distributions across various domains or combine multiple single-sourc...

Unsupervised domain adaptive segmentation algorithm based on two-level category alignment.

Neural networks : the official journal of the International Neural Network Society
To enhance the model's generalization ability in unsupervised domain adaptive segmentation tasks, most approaches have primarily focused on pixel-level local features, but neglected the clue in category information. This limitation results in the seg...

Synthetic CT generation based on CBCT using improved vision transformer CycleGAN.

Scientific reports
Cone-beam computed tomography (CBCT) is a crucial component of adaptive radiation therapy; however, it frequently encounters challenges such as artifacts and noise, significantly constraining its clinical utility. While CycleGAN is a widely employed ...

Structure enhanced prototypical alignment for unsupervised cross-domain node classification.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have demonstrated remarkable success in graph node classification task. However, their performance heavily relies on the availability of high-quality labeled data, which can be time-consuming and labor-intensive to acquir...

Missing Wedge Completion via Unsupervised Learning with Coordinate Networks.

International journal of molecular sciences
Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, whi...

Unsupervised machine learning for clustering forward head posture, protraction and retraction movement patterns based on craniocervical angle data in individuals with nonspecific neck pain.

BMC musculoskeletal disorders
OBJECTIVES: The traditional understanding of craniocervical alignment emphasizes specific anatomical landmarks. However, recent research has challenged the reliance on forward head posture as the primary diagnostic criterion for neck pain. An advance...

A retrospective prognostic evaluation using unsupervised learning in the treatment of COVID-19 patients with hypertension treated with ACEI/ARB drugs.

PeerJ
INTRODUCTION: This study aimed to evaluate the prognosis of patients with COVID-19 and hypertension who were treated with angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor B (ARB) drugs and to identify key features affecting patient...

DFUSNN: zero-shot dual-domain fusion unsupervised neural network for parallel MRI reconstruction.

Physics in medicine and biology
. Recently, deep learning models have been used to reconstruct parallel magnetic resonance (MR) images from undersampled k-space data. However, most existing approaches depend on large databases of fully sampled MR data for training, which can be cha...