AIMC Topic: Unsupervised Machine Learning

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Identification and Classification of Images in e-Cigarette-Related Content on TikTok: Unsupervised Machine Learning Image Clustering Approach.

Substance use & misuse
BACKGROUND: Previous studies identified e-cigarette content on popular video and image-based social media platforms such as TikTok. While machine learning approaches have been increasingly used with text-based social media data, image-based analysis ...

Unsupervised inter-domain transformation for virtually stained high-resolution mid-infrared photoacoustic microscopy using explainable deep learning.

Nature communications
Mid-infrared photoacoustic microscopy can capture biochemical information without staining. However, the long mid-infrared optical wavelengths make the spatial resolution of photoacoustic microscopy significantly poorer than that of conventional conf...

Domain-guided conditional diffusion model for unsupervised domain adaptation.

Neural networks : the official journal of the International Neural Network Society
Limited transferability hinders the performance of a well-trained deep learning model when applied to new application scenarios. Recently, Unsupervised Domain Adaptation (UDA) has achieved significant progress in addressing this issue via learning do...

Detecting cardiovascular diseases using unsupervised machine learning clustering based on electronic medical records.

BMC medical research methodology
BACKGROUND: Electronic medical records (EMR)-trained machine learning models have the potential in CVD risk prediction by integrating a range of medical data from patients, facilitate timely diagnosis and classification of CVDs. We tested the hypothe...

Determining structures of RNA conformers using AFM and deep neural networks.

Nature
Much of the human genome is transcribed into RNAs, many of which contain structural elements that are important for their function. Such RNA molecules-including those that are structured and well-folded-are conformationally heterogeneous and flexible...

Deep profiling of gene expression across 18 human cancers.

Nature biomedical engineering
Clinical and biological information in large datasets of gene expression across cancers could be tapped with unsupervised deep learning. However, difficulties associated with biological interpretability and methodological robustness have made this im...

Piecing together the narrative of #longcovid: an unsupervised deep learning of 1,354,889 X (formerly Twitter) posts from 2020 to 2023.

Frontiers in public health
OBJECTIVE: To characterize the public conversations around long COVID, as expressed through X (formerly Twitter) posts from May 2020 to April 2023.

Subspace learning using low-rank latent representation learning and perturbation theorem: Unsupervised gene selection.

Computers in biology and medicine
In recent years, gene expression data analysis has gained growing significance in the fields of machine learning and computational biology. Typically, microarray gene datasets exhibit a scenario where the number of features exceeds the number of samp...

Nongenerative Artificial Intelligence in Medicine: Advancements and Applications in Supervised and Unsupervised Machine Learning.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
The use of artificial intelligence (AI) within pathology and health care has advanced extensively. We have accordingly witnessed an increased adoption of various AI tools that are transforming our approach to clinical decision support, personalized m...

Unsupervised Bayesian generation of synthetic CT from CBCT using patient-specific score-based prior.

Medical physics
BACKGROUND: Cone-beam computed tomography (CBCT) scans, performed fractionally (e.g., daily or weekly), are widely utilized for patient alignment in the image-guided radiotherapy (IGRT) process, thereby making it a potential imaging modality for the ...