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Unsupervised Machine Learning

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Variability of morphology in photoplethysmographic waveform quantified with unsupervised wave-shape manifold learning for clinical assessment.

Physiological measurement
We investigated fluctuations of the photoplethysmography (PPG) waveform in patients undergoing surgery. There is an association between the morphologic variation extracted from arterial blood pressure (ABP) signals and short-term surgical outcomes. T...

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

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
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...

Unsupervised and Self-supervised Learning in Low-Dose Computed Tomography Denoising: Insights from Training Strategies.

Journal of imaging informatics in medicine
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...

Selective consistency of recurrent neural networks induced by plasticity as a mechanism of unsupervised perceptual learning.

PLoS computational biology
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...

FPL+: Filtered Pseudo Label-Based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation.

IEEE transactions on medical imaging
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...

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)
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 ...

UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples.

Communications biology
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...

Backdoor attacks on unsupervised graph representation learning.

Neural networks : the official journal of the International Neural Network Society
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...

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
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...

An Unsupervised Learning Tool for Plaque Tissue Characterization in Histopathological Images.

Sensors (Basel, Switzerland)
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...