AIMC Topic: Deep Learning

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Separable integral neural networks.

Neural networks : the official journal of the International Neural Network Society
Integral neural networks adopt continuous integral operators instead of conventional discrete convolutional operations to perform deep learning tasks. As this integral operator is the continuous representation of the regular convolutional operation, ...

Multi-scale graph harmonies: Unleashing U-Net's potential for medical image segmentation through contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Medical image segmentation is essential for accurately representing tissues and organs in scans, improving diagnosis, guiding treatment, enabling quantitative analysis, and advancing AI-assisted healthcare. Organs and lesion areas in medical images h...

Investigating streetscape environmental characteristics associated with road traffic crashes using street view imagery and computer vision.

Accident; analysis and prevention
Examining the relationship between streetscape features and road traffic crashes is vital for enhancing roadway safety. Traditional field surveys are often inefficient and lack comprehensive spatial coverage. Leveraging street view images (SVIs) and ...

Artificial intelligence-powered image analysis: A paradigm shift in infectious disease detection.

Artificial intelligence in medicine
The global burden of infectious diseases significantly affects mortality rates, with their varying symptoms making it challenging to assess and determine the severity of infections. Different countries face unique challenges related to these diseases...

Generative modeling of the Circle of Willis using 3D-StyleGAN.

NeuroImage
The circle of Willis (CoW) is a network of cerebral arteries with significant inter-individual anatomical variations. Deep learning has been used to characterize and quantify the status of the CoW in various applications for the diagnosis and treatme...

Exploratory study on the enhancement of O-RADS application effectiveness for novice ultrasonographers via deep learning.

Archives of gynecology and obstetrics
PURPOSE: The study aimed to create a deep convolutional neural network (DCNN) model based on ConvNeXt-Tiny to identify classic benign lesions (CBL) from other lesions (OL) within the Ovarian-Adnexal Reporting and Data System (O-RADS), enhancing the s...

Improved facial emotion recognition model based on a novel deep convolutional structure.

Scientific reports
Facial Emotion Recognition (FER) is a very challenging task due to the varying nature of facial expressions, occlusions, illumination, pose variations, cultural and gender differences, and many other aspects that cause a drastic degradation in qualit...

An ultrasonography of thyroid nodules dataset with pathological diagnosis annotation for deep learning.

Scientific data
Ultrasonography (US) of thyroid nodules is often time consuming and may be inconsistent between observers, with a low positivity rate for malignancy in biopsies. Even after determining the ultrasound Thyroid Imaging Reporting and Data System (TIRADS)...

The Pivotal Role of Baseline LDCT for Lung Cancer Screening in the Era of Artificial Intelligence.

Archivos de bronconeumologia
In this narrative review, we address the ongoing challenges of lung cancer (LC) screening using chest low-dose computerized tomography (LDCT) and explore the contributions of artificial intelligence (AI), in overcoming them. We focus on evaluating th...