AIMC Topic: Supervised Machine Learning

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Automated Detection of the Black Hole Sign for Patients with Intracerebral Hemorrhage Using Self-Supervised Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Intracerebral hemorrhage is a devastating form of stroke. Hematoma expansion (HE), growth of the hematoma on interval scans, predicts death and disability. Accurate prediction of HE is crucial for targeted interventions to imp...

Risk Prediction of Low Bone Density in Elderly Patients with Supervised Machine Learning Algorithms.

Balkan medical journal
BACKGROUND: Low bone mineral density (BMD) is a common age-related condition that elevates the risk of fractures and mortality. Machine learning (ML) techniques offer a promising approach for early prediction using readily available clinical, biochem...

Multi-View Self-Supervised Learning Enhances Automatic Sleep Staging From EEG Signals.

IEEE transactions on bio-medical engineering
Deep learning-based methods for automatic sleep staging offer an efficient and objective alternative to costly manual scoring. However, their reliance on extensive labeled datasets and the challenge of generalization to new subjects and datasets limi...

Differentiating estuarine dissolved organic matter composition by unsupervised and supervised machine learning.

Water research
Differentiating the composition of Dissolved Organic Matter (DOM) in estuaries is a major environmental concern, as the DOM characteristics are closely linked to biogeochemical and ecological considerations (e.g. water properties and trophic cycling)...

ECG synthesis for cardiac arrhythmias: Integrating self-supervised learning and generative adversarial networks.

Artificial intelligence in medicine
Arrhythmia classifiers relying on supervised deep learning models usually require a substantial amount of labeled clinical data. The distribution of these labels is strictly related to the statistics of cardiovascular diseases among the population, w...

Emergence of human-like attention and distinct head clusters in self-supervised vision transformers: A comparative eye-tracking study.

Neural networks : the official journal of the International Neural Network Society
Visual attention models aim to predict human gaze behavior, yet traditional saliency models and deep gaze prediction networks face limitations. Saliency models rely on handcrafted low-level visual features, often failing to capture human gaze dynamic...

Supervised machine learning and molecular docking modeling to identify potential Anti-Parkinson's agents.

Journal of molecular graphics & modelling
Parkinson's disease is a neurodegenerative condition that affects the brain's neurons, and causes malfunction of nerve cells and their death. A neurotransmitter called dopamine interacts with the part of the brain in charge of coordination and moveme...

Self-supervised learning for MRI reconstruction through mapping resampled k-space data to resampled k-space data.

Magnetic resonance imaging
In recent years, significant advancements have been achieved in applying deep learning (DL) to magnetic resonance imaging (MRI) reconstruction, which traditionally relies on fully sampled data. However, real-world clinical scenarios often demonstrate...

Certainty-Guided Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation.

IEEE transactions on bio-medical engineering
Semi-supervised learning (SSL) enables the accurate segmentation of medical images with limited available labeled data. However, its performance usually lags fully supervised methods that require the whole dataset to be labeled. We propose a novel SS...