AIMC Topic: Deep Learning

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Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing.

European journal of epidemiology
Deep Neural Networks (DNN) have been recently developed for the estimation of Biological Age (BA), the hypothetical underlying age of an organism, which can differ from its chronological age (CA). Although promising, these population-specific algorit...

Deep learning based spectral CT imaging.

Neural networks : the official journal of the International Neural Network Society
Spectral computed tomography (CT) has attracted much attention in radiation dose reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray energy spectrum is divided into several bins, each energy-bin-specific p...

SCU-Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms.

Medical physics
PURPOSE: Measurements of breast arterial calcifications (BAC) can offer a personalized, non-invasive approach to risk-stratify women for cardiovascular diseases such as heart attack and stroke. We aim to detect and segment breast arterial calcificati...

EVA: Evaluation of Metabolic Feature Fidelity Using a Deep Learning Model Trained With Over 25000 Extracted Ion Chromatograms.

Analytical chemistry
Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data relies on the recognition of extracted ion chromatogram (EIC) peak shapes using peak picking algorithms. Unfortunately, all peak picking algorithms present a sign...

Hybrid COVID-19 segmentation and recognition framework (HMB-HCF) using deep learning and genetic algorithms.

Artificial intelligence in medicine
COVID-19 (Coronavirus) went through a rapid escalation until it became a pandemic disease. The normal and manual medical infection discovery may take few days and therefore computer science engineers can share in the development of the automatic diag...

Fused CNN-LSTM deep learning emotion recognition model using electroencephalography signals.

The International journal of neuroscience
The traditional machine learning-based emotion recognition models have shown effective performance for classifying Electroencephalography (EEG) based emotions. The different machine learning algorithms outperform the various EEG based emotion models...

Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study.

European radiology
OBJECTIVES: To evaluate the performance of a deep learning radiomic nomogram (DLRN) model at predicting tumor relapse in patients with soft tissue sarcomas (STS) who underwent surgical resection.

Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering.

Microscopy research and technique
Melanoma skin cancer is the most life-threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effective...