AIMC Topic: Deep Learning

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D2C-Morph: Brain regional segmentation based on unsupervised registration network with similarity analysis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Brain regional segmentation is an image-processing approach widely used in brain image analyses. Deep learning models that perform segmentation alone play an important role in medical fields such as automatic diagnosis and prognosis prediction. This ...

U-Net-based architecture with attention mechanisms and Bayesian Optimization for brain tumor segmentation using MR images.

Computers in biology and medicine
As technological innovation in computers has advanced, radiologists may now diagnose brain tumors (BT) with the use of artificial intelligence (AI). In the medical field, early disease identification enables further therapies, where the use of AI sys...

Learning salient representation of crashes and near-crashes using supervised contrastive variational autoencoder.

Accident; analysis and prevention
Models capable of learning representations that are salient in safety-critical events (SCEs; including crashes and near-crashes) are crucial for road safety. This study proposes a novel deep learning model, the supervised contrastive variational auto...

Enhanced deep learning framework for real-time instrument detection and tracking in laparoscopic surgery using advanced augmentation and tracking techniques.

Surgical endoscopy
BACKGROUND: Accurate tracking and enumeration of surgical instruments are critical for patient safety and operational efficiency in laparoscopic procedures. Advanced tracking systems enhance object detection by maintaining instrument identity despite...

TGEL-transformer: Fusing educational theories with deep learning for interpretable student performance prediction.

PloS one
With the integration of educational technology and artificial intelligence, personalized learning has become increasingly important. However, traditional educational data mining methods struggle to effectively integrate heterogeneous feature data and...

A novel speech signal feature extraction technique to detect speech impairment in children accurately.

Computers in biology and medicine
Speech signal processing and extracting useful information from speech signal is necessary for speech language impairment (SLI) detection in children. Although different features has been suggested for SLI detection, there is still a scope exist for ...

Novel Artificial Intelligence-Driven Infant Meningitis Screening From High-Resolution Ultrasound Imaging.

Ultrasound in medicine & biology
BACKGROUND: Infant meningitis can be a life-threatening disease and requires prompt and accurate diagnosis to prevent severe outcomes or death. Gold-standard diagnosis requires lumbar puncture (LP) to obtain and analyze cerebrospinal fluid (CSF). Des...

Deep Learning-Based Automated Detection of the Middle Cerebral Artery in Transcranial Doppler Ultrasound Examinations.

Ultrasound in medicine & biology
OBJECTIVE: Transcranial Doppler (TCD) ultrasound has significant clinical value for assessing cerebral hemodynamics, but its reliance on operator expertise limits broader clinical adoption. In this work, we present a lightweight real-time deep learni...

Identification of novel biomarkers linked to M1 macrophage infiltration in the diagnosis of inflammatory bowel diseases.

International immunopharmacology
Inflammatory bowel disease (IBD) often lacks a definitive diagnostic standard, leading to diagnoses through exclusion. This study aimed to create a predictive model for IBD using bioinformatics and deep learning while identifying potential biomarkers...