AIMC Topic: Biometry
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Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data.
Scientific reports
Sep 1, 2022
With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical datasets became a key factor to enable reproducible development of diagnostic algorithms in the medical domain. Medical data contains ...
Advances in Vision-Based Gait Recognition: From Handcrafted to Deep Learning.
Sensors (Basel, Switzerland)
Jul 29, 2022
Identifying people's identity by using behavioral biometrics has attracted many researchers' attention in the biometrics industry. Gait is a behavioral trait, whereby an individual is identified based on their walking style. Over the years, gait reco...
Electrocardiogram Biometrics Using Transformer's Self-Attention Mechanism for Sequence Pair Feature Extractor and Flexible Enrollment Scope Identification.
Sensors (Basel, Switzerland)
Apr 30, 2022
The existing electrocardiogram (ECG) biometrics do not perform well when ECG changes after the enrollment phase because the feature extraction is not able to relate ECG collected during enrollment and ECG collected during classification. In this rese...
Template-Driven Knowledge Distillation for Compact and Accurate Periocular Biometrics Deep-Learning Models.
Sensors (Basel, Switzerland)
Mar 1, 2022
This work addresses the challenge of building an accurate and generalizable periocular recognition model with a small number of learnable parameters. Deeper (larger) models are typically more capable of learning complex information. For this reason, ...
Deep learning fetal ultrasound video model match human observers in biometric measurements.
Physics in medicine and biology
Feb 16, 2022
This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circumference and femur length, and to estimate gestati...
Prediction of the axial lens position after cataract surgery using deep learning algorithms and multilinear regression.
Acta ophthalmologica
Feb 6, 2022
BACKGROUND: The prediction of anatomical axial intraocular lens position (ALP) is one of the major challenges in cataract surgery. The purpose of this study was to develop and test prediction algorithms for ALP based on deep learning strategies.
Random vector functional link with ε-insensitive Huber loss function for biomedical data classification.
Computer methods and programs in biomedicine
Jan 6, 2022
BACKGROUND AND OBJECTIVE: Biomedical data classification has been a trending topic among researchers during the last decade. Biomedical datasets may contain several features noises. Hence, the conventional machine learning model cannot efficiently ha...
System on Chip (SoC) for Invisible Electrocardiography (ECG) Biometrics.
Sensors (Basel, Switzerland)
Jan 4, 2022
Biometric identification systems are a fundamental building block of modern security. However, conventional biometric methods cannot easily cope with their intrinsic security liabilities, as they can be affected by environmental factors, can be easil...
Prediction of corneal back surface power - Deep learning algorithm versus multivariate regression.
Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)
Nov 2, 2021
BACKGROUND: The corneal back surface is known to add some against the rule astigmatism, with implications in cataract surgery with toric lens implantation. This study aimed to set up and validate a deep learning algorithm to predict corneal back surf...
Anterior segment biometric measurements explain misclassifications by a deep learning classifier for detecting gonioscopic angle closure.
The British journal of ophthalmology
Oct 6, 2021
BACKGROUND/AIMS: To identify biometric parameters that explain misclassifications by a deep learning classifier for detecting gonioscopic angle closure in anterior segment optical coherence tomography (AS-OCT) images.
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