AIMC Topic: Deep Learning

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TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Processing of medical images such as MRI or CT presents different challenges compared to RGB images typically used in computer vision. These include a lack of labels for large datasets, high computational costs, and the need...

Speech signal enhancement in cocktail party scenarios by deep learning based virtual sensing of head-mounted microphones.

Hearing research
The cocktail party effect refers to the human sense of hearing's ability to pay attention to a single conversation while filtering out all other background noise. To mimic this human hearing ability for people with hearing loss, scientists integrate ...

Investigation and Highly Accurate Prediction of Missed Tryptic Cleavages by Deep Learning.

Journal of proteome research
Trypsin is one of the most important and widely used proteolytic enzymes in mass spectrometry (MS)-based proteomic research. It exclusively cleaves peptide bonds at the C-terminus of lysine and arginine. However, the cleavage is also affected by seve...

Deep learning of HIV field-based rapid tests.

Nature medicine
Although deep learning algorithms show increasing promise for disease diagnosis, their use with rapid diagnostic tests performed in the field has not been extensively tested. Here we use deep learning to classify images of rapid human immunodeficienc...

Clinically Verified Hybrid Deep Learning System for Retinal Ganglion Cells Aware Grading of Glaucomatous Progression.

IEEE transactions on bio-medical engineering
OBJECTIVE: Glaucoma is the second leading cause of blindness worldwide. Glaucomatous progression can be easily monitored by analyzing the degeneration of retinal ganglion cells (RGCs). Many researchers have screened glaucoma by measuring cup-to-disc ...

A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm.

Scientific reports
Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-bas...

Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer's disease, mild cognitive impairment and healthy ageing.

Journal of neural engineering
This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI) subjects and healthy ageing (HA) subjects using resting-state scalp electroencephalogram (E...

Deep learning-based photoplethysmography classification for peripheral arterial disease detection: a proof-of-concept study.

Physiological measurement
A proof-of-concept study to assess the potential of a deep learning (DL) based photoplethysmography PPG ('DLPPG') classification method to detect peripheral arterial disease (PAD) using toe PPG signals.PPG spectrogram images derived from our previous...

Crop yield prediction integrating genotype and weather variables using deep learning.

PloS one
Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop producti...

Deep-Learning-Based Emergency Stop Prediction for Robotic Lower-Limb Rehabilitation Training Systems.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Robotic lower-limb rehabilitation training is a better alternative for the physical training efforts of a therapist due to advantages, such as intensive repetitive motions, economical therapy, and quantitative assessment of the level of motor recover...