AIMC Topic: Unsupervised Machine Learning
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SequenceMorph: A Unified Unsupervised Learning Framework for Motion Tracking on Cardiac Image Sequences.
IEEE transactions on pattern analysis and machine intelligence
Jun 30, 2023
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)
Jun 20, 2023
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)
Jun 15, 2023
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...
Streamline unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes.
Molecular plant
May 18, 2023
Unsupervised machine learning framework for discriminating major variants of concern during COVID-19.
PloS one
May 18, 2023
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
May 8, 2023
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)
Apr 22, 2023
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
Apr 12, 2023
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
Apr 1, 2023
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...
Decoding reappraisal and suppression from neural circuits: A combined supervised and unsupervised machine learning approach.
Cognitive, affective & behavioral neuroscience
Mar 28, 2023
Emotion regulation is a core construct of mental health and deficits in emotion regulation abilities lead to psychological disorders. Reappraisal and suppression are two widely studied emotion regulation strategies but, possibly due to methodological...
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