AIMC Topic: Unsupervised Machine Learning
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Populational and individual information based PET image denoising using conditional unsupervised learning.
Physics in medicine and biology
Jul 19, 2021
Our study aims to improve the signal-to-noise ratio of positron emission tomography (PET) imaging using conditional unsupervised learning. The proposed method does not require low- and high-quality pairs for network training which can be easily appli...
An Unsupervised Learning-Based Spatial Co-Location Detection System from Low-Power Consumption Sensor.
Sensors (Basel, Switzerland)
Jul 13, 2021
Spatial co-location detection is the task of inferring the co-location of two or more objects in the geographic space. Mobile devices, especially a smartphone, are commonly employed to accomplish this task with the human object. Previous work focused...
An active learning approach for clustering single-cell RNA-seq data.
Laboratory investigation; a journal of technical methods and pathology
Jul 9, 2021
Single-cell RNA sequencing (scRNA-seq) data has been widely used to profile cellular heterogeneities with a high-resolution picture. Clustering analysis is a crucial step of scRNA-seq data analysis because it provides a chance to identify and uncover...
Exploring the potential of utilizing unsupervised machine learning for urban drainage sensor placement under future rainfall uncertainty.
Journal of environmental management
Jul 9, 2021
Recently, advanced informatics and sensing techniques show promise of enabling a new generation of smart stormwater systems, where real-time sensors are deployed to detect flooding hotspots. Existing stormwater design criteria assume that historical ...
Nothing about us without us: involving patient collaborators for machine learning applications in rheumatology.
Annals of the rheumatic diseases
Jul 5, 2021
Novel machine learning methods open the door to advances in rheumatology through application to complex, high-dimensional data, otherwise difficult to analyse. Results from such efforts could provide better classification of disease, decision support...
No-Reference Screen Content Image Quality Assessment With Unsupervised Domain Adaptation.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Jun 9, 2021
In this paper, we quest the capability of transferring the quality of natural scene images to the images that are not acquired by optical cameras (e.g., screen content images, SCIs), rooted in the widely accepted view that the human visual system has...
Self-Training With Progressive Representation Enhancement for Unsupervised Cross-Domain Person Re-Identification.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Jun 2, 2021
In recent years, person re-identification (re-ID) has achieved relatively good performance, benefiting from the revival of deep neural networks. However, due to the existence of domain bias which refers to the different data distributions between two...
Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study.
Journal of medical Internet research
May 27, 2021
BACKGROUND: Early detection and intervention are the key factors for improving outcomes in patients with COVID-19.
Integrative learning for population of dynamic networks with covariates.
NeuroImage
May 20, 2021
Although there is a rapidly growing literature on dynamic connectivity methods, the primary focus has been on separate network estimation for each individual, which fails to leverage common patterns of information. We propose novel graph-theoretic ap...
LCC-Net: A Lightweight Cross-Consistency Network for Semisupervised Cardiac MR Image Segmentation.
Computational and mathematical methods in medicine
May 17, 2021
Semantic segmentation plays a crucial role in cardiac magnetic resonance (MR) image analysis. Although supervised deep learning methods have made significant performance improvements, they highly rely on a large amount of pixel-wise annotated data, w...
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