AIMC Topic: Image Processing, Computer-Assisted

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Deep Ensemble for Central Serous Microscopic Retinopathy Detection in Retinal Optical Coherence Tomographic Images.

Microscopy research and technique
The retina is an important part of the eye that aids in focusing light and visual recognition to the brain. Hence, its damage causes vision loss in the human eye. Central serous retinopathy is a common retinal disorder in which serous detachment occu...

AutoDPS: An unsupervised diffusion model based method for multiple degradation removal in MRI.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Diffusion models have demonstrated their ability in image generation and solving inverse problems like restoration. Unlike most existing deep-learning based image restoration techniques which rely on unpaired or paired data ...

Artificial intelligence-enabled lipid droplets quantification: Comparative analysis of NIS-elements Segment.ai and ZeroCostDL4Mic StarDist networks.

Methods (San Diego, Calif.)
Lipid droplets (LDs) are dynamic organelles that are present in almost all cell types, with a particularly high prevalence in adipocytes. The phenotype of LDs in these cells reflects their maturity, metabolic activity and function. Although LDs quant...

Automatic analysis of high, medium, and low activities of broilers with heat stress operations via image processing and machine learning.

Poultry science
Heat stress is a major welfare problem in the poultry industry, altering broilers' activity levels. Advancements in image processing and machine learning provide opportunities to automatically quantify and analyze broiler activity. This study aimed t...

Joint Driver State Classification Approach: Face Classification Model Development and Facial Feature Analysis Improvement.

Sensors (Basel, Switzerland)
Driver drowsiness remains a critical factor in road safety, necessitating the development of robust detection methodologies. This study presents a dual-framework approach that integrates a convolutional neural network (CNN) and a facial landmark anal...

A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases.

Scientific reports
Detecting cassava leaf disease is challenging because it is hard to identify diseases accurately through visual inspection. Even trained agricultural experts may struggle to diagnose the disease correctly which leads to potential misjudgements. Tradi...

T1-weighted MRI-based brain tumor classification using hybrid deep learning models.

Scientific reports
Health is fundamental to human well-being, with brain health particularly critical for cognitive functions. Magnetic resonance imaging (MRI) serves as a cornerstone in diagnosing brain health issues, providing essential data for healthcare decisions....

Deeply supervised two stage generative adversarial network for stain normalization.

Scientific reports
The color variations present in histopathological images pose a significant challenge to computational pathology and, consequently, negatively affect the performance of certain pathological image analysis methods, especially those based on deep learn...

An AI-based tool for prosthetic crown segmentation serving automated intraoral scan-to-CBCT registration in challenging high artifact scenarios.

The Journal of prosthetic dentistry
STATEMENT OF PROBLEM: Accurately registering intraoral and cone beam computed tomography (CBCT) scans in patients with metal artifacts poses a significant challenge. Whether a cloud-based platform trained for artificial intelligence (AI)-driven segme...

Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.

European journal of nuclear medicine and molecular imaging
PURPOSE: Respiratory motion during PET acquisition may produce lesion blurring. Ultra-fast 20-second breath-hold (U2BH) PET reduces respiratory motion artifacts, but the shortened scanning time increases statistical noise and may affect diagnostic qu...