AIMC Topic: Deep Learning

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Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons.

Nature communications
In order to better understand how the brain perceives faces, it is important to know what objective drives learning in the ventral visual stream. To answer this question, we model neural responses to faces in the macaque inferotemporal (IT) cortex wi...

Identification of asthma control factor in clinical notes using a hybrid deep learning model.

BMC medical informatics and decision making
BACKGROUND: There are significant variabilities in guideline-concordant documentation in asthma care. However, assessing clinician's documentation is not feasible using only structured data but requires labor-intensive chart review of electronic heal...

Dynamic stochastic deep learning approaches for predicting geometric changes in head and neck cancer.

Physics in medicine and biology
Modern radiotherapy stands to benefit from the ability to efficiently adapt plans during treatment in response to setup and geometric variations such as those caused by internal organ deformation or tumor shrinkage. A promising strategy is to develop...

Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images.

Computational and mathematical methods in medicine
Early diagnosis of the harmful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with clinical expertise, allows governments to break the transition chain and flatten the epidemic curve. Although reverse transcription-polymerase cha...

Diagnosis of Schizophrenia Based on Deep Learning Using fMRI.

Computational and mathematical methods in medicine
Schizophrenia is a brain disease that frequently occurs in young people. Early diagnosis and treatment can reduce family burdens and reduce social costs. There is no objective evaluation index for schizophrenia. In order to improve the classification...

A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.

PloS one
INTRODUCTION: Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH). We propose applying Machine Learning (ML), and Deep Learning (DL) ...

Synergy-Based Neural Interface for Human Gait Tracking With Deep Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Neural information decomposed from electromyography (EMG) signals provides a new path of EMG-based human-machine interface. Instead of the motor unit decomposition-based method, this work presents a novel neural interface for human gait tracking base...

Using infrared imaging and deep learning in fit-checking of respiratory protective devices among healthcare professionals.

Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
AIMS: This study aimed to investigate the application of infrared thermal imaging and adopt deep learning to detect air leakage for determining the fitness of respirators during fit-checks.

Performance comparison of deep learning and machine learning methods in determining wetland water areas using EuroSAT dataset.

Environmental science and pollution research international
Wetlands are critical to the ecology because they maintain biodiversity and provide home for a variety of species. Researching, mapping, and conservation of wetlands is a challenging and time-consuming process. Because they produce temporal and geogr...

ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation.

Circulation
BACKGROUND: Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predi...