AIMC Topic: Deep Learning

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Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets.

PLoS computational biology
Segmenting three-dimensional (3D) microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth, and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed, which claim ...

Deep learning -- promises for 3D nuclear imaging: a guide for biologists.

Journal of cell science
For the past century, the nucleus has been the focus of extensive investigations in cell biology. However, many questions remain about how its shape and size are regulated during development, in different tissues, or during disease and aging. To trac...

COVID Detection From Chest X-Ray Images Using Multi-Scale Attention.

IEEE journal of biomedical and health informatics
Deep learning based methods have shown great promise in achieving accurate automatic detection of Coronavirus Disease (covid) - 19 from Chest X-Ray (cxr) images.However, incorporating explainability in these solutions remains relatively less explored...

Deep Learning Based Centerline-Aggregated Aortic Hemodynamics: An Efficient Alternative to Numerical Modeling of Hemodynamics.

IEEE journal of biomedical and health informatics
Image-based patient-specific modelling of hemodynamics are gaining increased popularity as a diagnosis and outcome prediction solution for a variety of cardiovascular diseases. While their potential to improve diagnostic capabilities and thereby clin...

Time-Frequency Analysis of Scalp EEG With Hilbert-Huang Transform and Deep Learning.

IEEE journal of biomedical and health informatics
Electroencephalography (EEG) is a brain imaging approach that has been widely used in neuroscience and clinical settings. The conventional EEG analyses usually require pre-defined frequency bands when characterizing neural oscillations and extracting...

Analysis of Basketball Technical Movements Based on Human-Computer Interaction with Deep Learning.

Computational intelligence and neuroscience
With the continuous development of computer technology, analysis techniques based on various types of sports data sets are also evolving. One typical representative is image-based motion recognition technology, which enables video action recognition ...

Application of Unsupervised Transfer Technique Based on Deep Learning Model in Physical Training.

Computational intelligence and neuroscience
The research purpose is to study the standardization and scientizing of physical training actions. Stacking denoising auto encoder (SDAE), a BiLSTM deep network model (SDAL-DNM) (a kind of training action model), and an unsupervised transfer model ar...

A visual deep learning model to predict abnormal versus normal parathyroid glands using intraoperative autofluorescence signals.

Journal of surgical oncology
BACKGROUND: Previous work demonstrated that abnormal versus normal parathyroid glands (PGs) exhibit different patterns of autofluorescence, with former appearing darker and more heterogenous. Our objective was to develop a visual artificial intellige...

Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM).

Sensors (Basel, Switzerland)
Emotions are an essential part of daily human communication. The emotional states and dynamics of the brain can be linked by electroencephalography (EEG) signals that can be used by the Brain-Computer Interface (BCI), to provide better human-machine ...

Performance of Deep Learning Models in Forecasting Gait Trajectories of Children with Neurological Disorders.

Sensors (Basel, Switzerland)
Forecasted gait trajectories of children could be used as feedforward input to control lower limb robotic devices, such as exoskeletons and actuated orthotic devices (e.g., Powered Ankle Foot Orthosis-PAFO). Several studies have forecasted healthy ga...