AIMC Topic: Deep Learning

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ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus.

Computers in biology and medicine
In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can signif...

Deciphering Protein Secondary Structures and Nucleic Acids in Cryo-EM Maps Using Deep Learning.

Journal of chemical information and modeling
With the resolution revolution of cryo-electron microscopy (cryo-EM) and the rapid development of image processing technology, cryo-EM has become an indispensable experimental method for determining the three-dimensional structures of biological macr...

Comparative Analysis of Recurrent Neural Networks with Conjoint Fingerprints for Skin Corrosion Prediction.

Journal of chemical information and modeling
Skin corrosion assessment is an essential toxicity end point that addresses safety concerns for topical dosage forms and cosmetic products. Previously, skin corrosion assessments required animal testing; however, differences in skin architecture and ...

Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiography.

EBioMedicine
BACKGROUND: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic stenosis (AS), yet it requires skilled operators and can be resource-intensive. We developed and validated an artificial intelligence (AI)-based system for...

A Deep Learning-Based Approach to Detect Lamina Dura Loss on Periapical Radiographs.

Journal of imaging informatics in medicine
This study aimed to develop a custom artificial intelligence (AI) model for detecting lamina dura (LD) loss around the roots of anterior and posterior teeth on intraoral periapical radiographs. A total of 701 periapical radiographs of the anterior an...

Development of an artificial intelligence system to indicate intraoperative findings of scarring in laparoscopic cholecystectomy for cholecystitis.

Surgical endoscopy
BACKGROUND: The surgical difficulty of laparoscopic cholecystectomy (LC) for acute cholecystitis (AC) and the risk of bile duct injury (BDI) depend on the degree of fibrosis and scarring caused by inflammation; therefore, understanding these intraope...

A Novel Deep Ensemble Method for Selective Classification of Electrocardiograms.

IEEE transactions on bio-medical engineering
OBJECTIVE: Telehealth paradigms are essential for remotely managing patients with chronic conditions. To assist clinicians in handling the large volumes of data collected through these systems, clinical decision support systems (CDSSs) have been deve...

TFTL: A Task-Free Transfer Learning Strategy for EEG-Based Cross-Subject and Cross-Dataset Motor Imagery BCI.

IEEE transactions on bio-medical engineering
OBJECTIVE: Motor imagery-based brain-computer interfaces (MI-BCIs) have been playing an increasingly vital role in neural rehabilitation. However, the long-term task-based calibration required for enhanced model performance leads to an unfriendly use...

Deep Learning for Pediatric Sleep Staging From Photoplethysmography: A Transfer Learning Approach From Adults to Children.

IEEE transactions on bio-medical engineering
BACKGROUND: Sleep staging is critical for diagnosing sleep disorders. Traditional methods in clinical settings involve time-intensive scoring procedures. Recent advancements in data-driven algorithms using photoplethysmogram (PPG) time series have sh...

Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy.

IEEE transactions on bio-medical engineering
OBJECTIVE: To develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modula...