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Unsupervised Machine Learning

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Unsupervised Learning of Graph Matching With Mixture of Modes via Discrepancy Minimization.

IEEE transactions on pattern analysis and machine intelligence
Graph matching (GM) has been a long-standing combinatorial problem due to its NP-hard nature. Recently (deep) learning-based approaches have shown their superiority over the traditional solvers while the methods are almost based on supervised learnin...

SequenceMorph: A Unified Unsupervised Learning Framework for Motion Tracking on Cardiac Image Sequences.

IEEE transactions on pattern analysis and machine intelligence
Modern medical imaging techniques, such as ultrasound (US) and cardiac magnetic resonance (MR) imaging, have enabled the evaluation of myocardial deformation directly from an image sequence. While many traditional cardiac motion tracking methods have...

Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring.

Sensors (Basel, Switzerland)
Wireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, water quality monitoring is a vital process to ensure the sustainable, important feeding of and as a li...

Underwater Target Detection Utilizing Polarization Image Fusion Algorithm Based on Unsupervised Learning and Attention Mechanism.

Sensors (Basel, Switzerland)
Since light propagation in water bodies is subject to absorption and scattering effects, underwater images using only conventional intensity cameras will suffer from low brightness, blurred images, and loss of details. In this paper, a deep fusion ne...

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19.

PloS one
Due to the high mutation rate of the virus, the COVID-19 pandemic evolved rapidly. Certain variants of the virus, such as Delta and Omicron emerged with altered viral properties leading to severe transmission and death rates. These variants burdened ...

Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning.

PloS one
PURPOSE: Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved m...

A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges.

Sensors (Basel, Switzerland)
Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine l...

Foundation models for generalist medical artificial intelligence.

Nature
The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities in medicine. We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI)....

Mine local homogeneous representation by interaction information clustering with unsupervised learning in histopathology images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The success of data-driven deep learning for histopathology images often depends on high-quality training sets and fine-grained annotations. However, as tumors are heterogeneous and annotations are expensive, unsupervised le...