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| http://dx.doi.org/10.3174/ajnr.A6092 | DOI Listing |
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Automated Detection of Filamentous Fungal Keratitis on Whole Slide Images of Potassium Hydroxide Smears with Multiple Instance Learning.
Ophthalmol Sci
November 2024
Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
Purpose: The diagnosis of fungal keratitis using potassium hydroxide (KOH) smears of corneal scrapings enables initiation of the correct antimicrobial therapy at the point-of-care but requires time-consuming manual examination and expertise. This study evaluates the efficacy of a deep learning framework, dual stream multiple instance learning (DSMIL), in automating the analysis of whole slide imaging (WSI) of KOH smears for rapid and accurate detection of fungal infections.
Design: Retrospective observational study.
LRNet: Link Residual Neural Network for Blood Vessel Segmentation in OCTA Images.
J Imaging Inform Med
January 2025
Department of Ophthalmology, The Affiliated Hospital of Guilin Medical University, Guilin, China.
Optical coherence tomography angiography (OCTA) is an emerging, non-invasive technique increasingly utilized for retinal vasculature imaging. Analysis of OCTA images can effectively diagnose retinal diseases, unfortunately, complex vascular structures within OCTA images possess significant challenges for automated segmentation. A novel, fully convolutional dense connected residual network is proposed to effectively segment the vascular regions within OCTA images.
View Article and Find Full Text PDFDevelopment of a Deep Learning-Based Model for Pressure Injury Surface Assessment.
J Clin Nurs
January 2025
School of Nursing, Jinan University, Guangzhou, Guangdong, China.
Aim: To develop a deep learning-based smart assessment model for pressure injury surface.
Design: Exploratory analysis study.
Methods: Pressure injury images from four Guangzhou hospitals were labelled and used to train a neural network model.
SD-LayerNet: Robust and label-efficient retinal layer segmentation via anatomical priors.
Comput Methods Programs Biomed
January 2025
Christian Doppler Laboratory for Artificial Intelligence in Retina, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria; Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.
Background And Objectives: Automated, anatomically coherent retinal layer segmentation in optical coherence tomography (OCT) is one of the most important components of retinal disease management. However, current methods rely on large amounts of labeled data, which can be difficult and expensive to obtain. In addition, these systems tend often propose anatomically impossible results, which undermines their clinical reliability.
View Article and Find Full Text PDFImproved deep canonical correlation fusion approach for detection of early mild cognitive impairment.
Med Biol Eng Comput
January 2025
Non-Invasive Imaging and Diagnostic Laboratory, Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, India.
Detection of early mild cognitive impairment (EMCI) is clinically challenging as it involves subtle alterations in multiple brain sub-anatomic regions. Among different brain regions, the corpus callosum and lateral ventricles are primarily affected due to EMCI. In this study, an improved deep canonical correlation analysis (CCA) based framework is proposed to fuse magnetic resonance (MR) image features from lateral ventricular and corpus callosal structures for the detection of EMCI condition.
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