AIMC Topic: Deep Learning

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A deep learning algorithm for automatic 3D segmentation and quantification of hamstrings musculotendon injury from MRI.

Scientific reports
In high-velocity sports, hamstring strain injuries are common causes of missed play and have high rates of reinjury. Evaluating the severity and location of a hamstring strain injury, currently graded by a clinician using a semiqualitative muscle inj...

Wearable interactive full-body motion tracking and haptic feedback network systems with deep learning.

Nature communications
The increasing demand for motion tracking systems has been accelerated by advancements in virtual reality (VR) and motion reconstruction technologies. Combined with emerging innovations in the Internet of Things (IoT), these systems have unlocked tra...

Automated deep U-Net model for ischemic stroke lesion segmentation in the sub-acute phase.

Scientific reports
Manual segmentation of sub-acute ischemic stroke lesions in fluid-attenuated inversion recovery magnetic resonance imaging (FLAIR MRI) is time-consuming and subject to inter-observer variability, limiting clinical workflow efficiency. To develop and ...

Metaheuristic-optimized swin transformer with SHAP explainability for keratoconus classification from corneal topography maps.

International ophthalmology
Keratoconus (KCN) is an uncommon corneal disorder where the central cornea undergoes advanced thinning and causes non-uniform astigmatism. This results in metamorphopsia and potential vision loss if it is left untreated. Early detection of KCN is maj...

Prediction of regional cropland soil organic carbon content and distribution using deep learning: a case study of the Northeast China Plain.

Environmental monitoring and assessment
Soil organic carbon (SOC) is a critical component of soil fertility and plays a significant role in global carbon sequestration. The decline in SOC content across global croplands poses significant challenges to both agricultural productivity and env...

Alzheimer's disease classification using a hybrid deep learning approach with multi-layer U-net segmentation and XAI driven analysis.

PloS one
Alzheimer's disease (AD) is a neurodegenerative illness causing a significant decrease in cognitive function, and early, accurate diagnosis is of great therapeutic and diagnostic value. Currently, there is promising potential for applying various typ...

Fusion of habitat analysis and deep learning on contrast-enhanced T1-weighted imaging for predicting Ki-67 status in pediatric brain tumors.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
PURPOSE: Tumors are heterogeneous and consist of subregions, also known as tumor habitats, each of which corresponds to a group of tissues with similar structural, metabolic or functional characteristics. This study aims to visualize and quantify int...

DeepMaT: Prediction of Target Peptide Classification and Cleavage Site by Combining Mamba2 and Multiple Attention Mechanisms.

Journal of chemical information and modeling
Signal peptides and transit peptides are essential for directing mature proteins to their proper cellular locations, particularly through cleavage following transport. Although various prediction tools achieve strong performance in identifying and cl...

CNSGT: Generative Transformer for De Novo Drug Design Targeting the Central Nervous System.

Journal of chemical information and modeling
The design of novel central nervous system (CNS) drugs presents formidable challenges due to the restrictive nature of the blood-brain barrier, which imposes stringent physicochemical requirements. Recent advances in deep learning, particularly Trans...

InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records.

Nature communications
Electronic health records contain multimodal data that can inform clinical decisions but are often unsuited for advanced machine learning analyses due to lack of labeled data. Here, we present InfEHR, a framework to automatically compute clinical lik...