AIMC Topic: Unsupervised Machine Learning

Clear Filters Showing 131 to 140 of 828 articles

Unsupervised reconstruction of accelerated cardiac cine MRI using neural fields.

Computers in biology and medicine
BACKGROUND: Cardiac cine MRI is the gold standard for cardiac functional assessment, but the inherently slow acquisition process creates the necessity of reconstruction approaches for accelerated undersampled acquisitions. Several regularization appr...

Unsupervised learning for lake underwater vegetation classification: Constructing high-precision, large-scale aquatic ecological datasets.

The Science of the total environment
Monitoring underwater vegetation is vital for evaluating lake ecosystem health. Automated data collection and analysis play key roles in achieving large-scale, high-precision, and high-frequency monitoring. While technologies such as unmanned vessels...

UDA-GS: A cross-center multimodal unsupervised domain adaptation framework for Glioma segmentation.

Computers in biology and medicine
Gliomas are the most common and malignant form of primary brain tumors. Accurate segmentation and measurement from MRI are crucial for diagnosis and treatment. Due to the infiltrative growth pattern of gliomas, their labeling is very difficult. In tu...

Illumination-Guided progressive unsupervised domain adaptation for low-light instance segmentation.

Neural networks : the official journal of the International Neural Network Society
Due to limited photons, low-light environments pose significant challenges for computer vision tasks. Unsupervised domain adaptation offers a potential solution, but struggles with domain misalignment caused by inadequate utilization of features at d...

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning.

Nature communications
Integrating diverse types of biological data is essential for a holistic understanding of cancer biology, yet it remains challenging due to data heterogeneity, complexity, and sparsity. Addressing this, our study introduces an unsupervised deep learn...

Unsupervised Learning-Assisted Acoustic-Driven Nano-Lens Holography for the Ultrasensitive and Amplification-Free Detection of Viable Bacteria.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Bacterial infection is a crucial factor resulting in public health issues worldwide, often triggering epidemics and even fatalities. The accurate, rapid, and convenient detection of viable bacteria is an effective method for reducing infections and i...

Anomaly-based threat detection in smart health using machine learning.

BMC medical informatics and decision making
BACKGROUND: Anomaly detection is crucial in healthcare data due to challenges associated with the integration of smart technologies and healthcare. Anomaly in electronic health record can be associated with an insider trying to access and manipulate ...

Smart data-driven medical decisions through collective and individual anomaly detection in healthcare time series.

International journal of medical informatics
BACKGROUND: Anomalies in healthcare refer to deviation from the norm of unusual or unexpected patterns or activities related to patients, diseases or medical centres. Detecting these anomalies is crucial for timely interventions and efficient decisio...

Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.

Circulation. Cardiovascular quality and outcomes
BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. ...