AIMC Topic: Deep Learning

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Deep Learning-Based Assessment Model for Real-Time Identification of Visual Learners Using Raw EEG.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Automatic identification of visual learning style in real time using raw electroencephalogram (EEG) is challenging. In this work, inspired by the powerful abilities of deep learning techniques, deep learning-based models are proposed to learn high-le...

Deep Learning-Based Identification Algorithm for Transitions Between Walking Environments Using Electromyography Signals Only.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Although studies on terrain identification algorithms to control walking assistive devices have been conducted using sensor fusion, studies on transition classification using only electromyography (EMG) signals have yet to be conducted. Therefore, th...

Domain transformation using semi-supervised CycleGAN for improving performance of classifying thyroid tissue images.

International journal of computer assisted radiology and surgery
PURPOSE: A large number of research has been conducted on the classification of medical images using deep learning. The thyroid tissue images can be also classified by cancer types. Deep learning requires a large amount of data, but every medical ins...

Enhancing Nigrosome-1 Sign Identification via Interpretable AI using True Susceptibility Weighted Imaging.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Nigrosome 1 (N1), the largest nigrosome region in the ventrolateral area of the substantia nigra pars compacta, is identifiable by the "N1 sign" in long echo time gradient echo MRI. The N1 sign's absence is a vital Parkinson's disease (PD...

An artificial intelligence tool predicts blastocyst development from static images of fresh mature oocytes.

Reproductive biomedicine online
RESEARCH QUESTION: Can a deep learning image analysis model be developed to assess oocyte quality by predicting blastocyst development from images of denuded mature oocytes?

Detection of circulating plasma cells in peripheral blood using deep learning-based morphological analysis.

Cancer
BACKGROUND: The presence of circulating plasma cells (CPCs) is an important laboratory indicator for the diagnosis, staging, risk stratification, and progression monitoring of multiple myeloma (MM). Early detection of CPCs in the peripheral blood (PB...

Predicting systemic diseases in fundus images: systematic review of setting, reporting, bias, and models' clinical availability in deep learning studies.

Eye (London, England)
BACKGROUND: Analyzing fundus images with deep learning techniques is promising for screening systematic diseases. However, the quality of the rapidly increasing number of studies was variable and lacked systematic evaluation.

Explainable deep learning-based survival prediction for non-small cell lung cancer patients undergoing radical radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Survival is frequently assessed using Cox proportional hazards (CPH) regression; however, CPH may be too simplistic as it assumes a linear relationship between covariables and the outcome. Alternative, non-linear machine learn...

A physics-informed deep learning framework for modeling of coronary in-stent restenosis.

Biomechanics and modeling in mechanobiology
Machine learning (ML) techniques have shown great potential in cardiovascular surgery, including real-time stenosis recognition, detection of stented coronary anomalies, and prediction of in-stent restenosis (ISR). However, estimating neointima evolu...