AIMC Topic: Unsupervised Machine Learning

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Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Sep 6, 2024
Uncovering fine-grained phenotypes of live cell dynamics is pivotal for a comprehensive understanding of the heterogeneity in healthy and diseased biological processes. However, this endeavor poses significant technical challenges for unsupervised ma...
Cell Movement Unsupervised Machine Learning Phenotype Deep Learning Humans Neural Networks, Computer
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Unsupervised and Self-supervised Learning in Low-Dose Computed Tomography Denoising: Insights from Training Strategies.

Journal of imaging informatics in medicine Sep 4, 2024
In recent years, X-ray low-dose computed tomography (LDCT) has garnered widespread attention due to its significant reduction in the risk of patient radiation exposure. However, LDCT images often contain a substantial amount of noises, adversely affe...
Signal-To-Noise Ratio Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Unsupervised Machine Learning Supervised Machine Learning Radiation Dosage Humans
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Selective consistency of recurrent neural networks induced by plasticity as a mechanism of unsupervised perceptual learning.

PLoS computational biology Sep 3, 2024
Understanding the mechanism by which the brain achieves relatively consistent information processing contrary to its inherent inconsistency in activity is one of the major challenges in neuroscience. Recently, it has been reported that the consistenc...
Neural Networks, Computer Brain Learning Humans Unsupervised Machine Learning Perception Nerve Net Computational Biology Neuronal Plasticity Models, Neurological
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FPL+: Filtered Pseudo Label-Based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation.

IEEE transactions on medical imaging Sep 3, 2024
Adapting a medical image segmentation model to a new domain is important for improving its cross-domain transferability, and due to the expensive annotation process, Unsupervised Domain Adaptation (UDA) is appealing where only unlabeled images are ne...
Humans Imaging, Three-Dimensional Algorithms Magnetic Resonance Imaging Databases, Factual Unsupervised Machine Learning Brain
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An unsupervised learning model based on CT radiomics features accurately predicts axillary lymph node metastasis in breast cancer patients: diagnostic study.

International journal of surgery (London, England) Sep 1, 2024
BACKGROUND: The accuracy of traditional clinical methods for assessing the metastatic status of axillary lymph nodes (ALNs) is unsatisfactory. In this study, the authors propose the use of radiomic technology and three-dimensional (3D) visualization ...
Unsupervised Machine Learning Predictive Value of Tests Lymphatic Metastasis Lymph Node Excision Axilla Adult Breast Neoplasms Tomography, X-Ray Computed Radiomics Humans Middle Aged Female Retrospective Studies Imaging, Three-Dimensional Aged Lymph Nodes
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UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples.

Communications biology Aug 30, 2024
Multiplexed imaging technologies have made it possible to interrogate complex tissue microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily an...
Humans Bayes Theorem Algorithms Deep Learning Cell Nucleus Image Processing, Computer-Assisted Unsupervised Machine Learning
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Backdoor attacks on unsupervised graph representation learning.

Neural networks : the official journal of the International Neural Network Society Aug 29, 2024
Unsupervised graph learning techniques have garnered increasing interest among researchers. These methods employ the technique of maximizing mutual information to generate representations of nodes and graphs. We show that these methods are susceptibl...
Neural Networks, Computer Algorithms Cluster Analysis Unsupervised Machine Learning Computer Graphics
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Unsupervised adversarial neural network for enhancing vasculature in photoacoustic tomography images using optical coherence tomography angiography.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Aug 28, 2024
Photoacoustic tomography (PAT) is a powerful imaging modality for visualizing tissue physiology and exogenous contrast agents. However, PAT faces challenges in visualizing deep-seated vascular structures due to light scattering, absorption, and reduc...
Photoacoustic Techniques Neural Networks, Computer Animals Tomography, Optical Coherence Pregnancy Mice Female Angiography Unsupervised Machine Learning
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An Unsupervised Learning Tool for Plaque Tissue Characterization in Histopathological Images.

Sensors (Basel, Switzerland) Aug 20, 2024
Stroke is the second leading cause of death and a major cause of disability around the world, and the development of atherosclerotic plaques in the carotid arteries is generally considered the leading cause of severe cerebrovascular events. In recent...
Plaque, Atherosclerotic Unsupervised Machine Learning Image Interpretation, Computer-Assisted Algorithms Humans Carotid Arteries Image Processing, Computer-Assisted
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Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure.

ESC heart failure Aug 19, 2024
AIMS: Hospitalized patients with heart failure (HF) are a heterogeneous population, with multiple phenotypes proposed. Prior studies have not examined the biological phenotypes of critically ill patients with HF admitted to the contemporary cardiac i...
Humans Phenotype Retrospective Studies Intensive Care Units Follow-Up Studies Female Unsupervised Machine Learning Heart Failure Coronary Care Units Male Aged Middle Aged Survival Rate Hospital Mortality
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