AIMC Topic: Humans

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FxTS-Net: Fixed-time stable learning framework for Neural ODEs.

Neural networks : the official journal of the International Neural Network Society
Neural Ordinary Differential Equations (Neural ODEs), as a novel category of modeling big data methods, cleverly link traditional neural networks and dynamical systems. However, it is challenging to ensure the dynamics system reaches a correctly pred...

Conventional and machine learning-based analysis of age, body weight and body height significance in knot position-related thyrohyoid and cervical spine fractures in suicidal hangings.

International journal of legal medicine
The thyrohyoid complex and cervical spine fracture distribution patterns may reflect the knot position as the force distribution by the noose to different neck regions may vary depending on it. Recently, machine learning models (MLm) were used to cla...

DLPVI: Deep learning framework integrating projection, view-by-view backprojection, and image domains for high- and ultra-sparse-view CBCT reconstruction.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
This study proposes a deep learning framework, DLPVI, which integrates projection, view-by-view backprojection (VVBP), and image domains to improve the quality of high-sparse-view and ultra-sparse-view cone-beam computed tomography (CBCT) images. The...

Efficient diagnosis of retinal disorders using dual-branch semi-supervised learning (DB-SSL): An enhanced multi-class classification approach.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The early diagnosis of retinal disorders is essential in preventing permanent or partial blindness. Identifying these conditions promptly guarantees early treatment and prevents blindness. However, the challenge lies in accurately diagnosing these co...

Meta-learning guidance for robust medical image synthesis: Addressing the real-world misalignment and corruptions.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Deep learning-based image synthesis for medical imaging is currently an active research topic with various clinically relevant applications. Recently, methods allowing training with misaligned data have started to emerge, yet current solution lack ro...

A comparative analysis of trauma-related mortality in South Korea using classification models.

International journal of medical informatics
BACKGROUND: Reducing mortality among severe trauma patients requires the establishment of an effective emergency transportation system and the rapid transfer of patients to appropriate medical facilities. Machine learning offers significant potential...

Age and gender-related changes in choroidal thickness: Insights from deep learning analysis of swept-source OCT images.

Photodiagnosis and photodynamic therapy
BACKGROUND: The choroid is a vital vascular layer of the eye, essential for maintaining ocular health. Understanding its structural variations, particularly choroidal thickness (CT), is crucial for the early detection of diseases, such as age-related...

Prediction of insulin resistance using multiple adaptive regression spline in Chinese women.

Endocrine journal
Insulin resistance (IR) is the core for type 2 diabetes and metabolic syndrome. The homeostasis assessment model is a straightforward and practical tool for quantifying insulin resistance (HOMA-IR). Multiple adaptive regression spline (MARS) is a mac...

Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging.

NeuroImage
INTRODUCTION: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian comp...

Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction.

Magma (New York, N.Y.)
OBJECT: Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements. The aim of this project is to reduce reconstruction...