AIMC Topic: Algorithms

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Differentiating Glaucomatous Optic Neuropathy From Non-glaucomatous Optic Neuropathies Using Deep Learning Algorithms.

American journal of ophthalmology
PURPOSE: A deep learning framework to differentiate glaucomatous optic disc changes due to glaucomatous optic neuropathy (GON) from non-glaucomatous optic disc changes due to non-glaucomatous optic neuropathies (NGONs).

Deep learning reconstruction with single-energy metal artifact reduction in pelvic computed tomography for patients with metal hip prostheses.

Japanese journal of radiology
PURPOSE: The aim of this study was to assess the impact of the deep learning reconstruction (DLR) with single-energy metal artifact reduction (SEMAR) (DLR-S) technique in pelvic helical computed tomography (CT) images for patients with metal hip pros...

A Two-Stage End-to-End Deep Learning Framework for Pathologic Examination in Skin Tumor Diagnosis.

The American journal of pathology
Neurofibromas (NFs), Bowen disease (BD), and seborrheic keratosis (SK) are common skin tumors. Pathologic examination is the gold standard for diagnosis of these tumors. Current pathologic diagnosis is primarily based on microscopic observation, whic...

Accurate detection of arrhythmias on raw electrocardiogram images: An aggregation attention multi-label model for diagnostic assistance.

Medical engineering & physics
BACKGROUND: The low rate of detection of abnormalities has been a major problem with current artificial intelligence-based electrocardiogram diagnostic algorithms, particularly when applied under real-world clinical scenarios.

A Fusion-Assisted Multi-Stream Deep Learning and ESO-Controlled Newton-Raphson-Based Feature Selection Approach for Human Gait Recognition.

Sensors (Basel, Switzerland)
The performance of human gait recognition (HGR) is affected by the partial obstruction of the human body caused by the limited field of view in video surveillance. The traditional method required the bounding box to recognize human gait in the video ...

Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique.

Sensors (Basel, Switzerland)
Human action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long S...

Devising a deep neural network based mammography phantom image filtering algorithm using images obtained under mAs and kVp control.

Scientific reports
We study whether deep neural network based algorithm can filter out mammography phantom images that will pass or fail. With 543 phantom images generated from a mammography unit, we created VGG16 based phantom shape scoring models (multi-and binary-cl...

Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.

Journal of medical Internet research
BACKGROUND: A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite ...

A deep-learning algorithm to classify skin lesions from mpox virus infection.

Nature medicine
Undetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of MPXV infection, we developed an image-based deep convolutional neu...

Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning.

IEEE transactions on medical imaging
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically releva...