AIMC Topic: Image Processing, Computer-Assisted

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Microscope-Assisted Hypertensive Retinopathy Diagnosis Using Deep Learning Models.

Microscopy research and technique
The retina is the most crucial part of the human eye, and it can be affected due to hypertension. However, retinal abnormalities due to hypertension are termed hypertensive retinopathy (HR). A severe stage of HR can lead to complete blindness if not ...

Application of automatic image analysis using a Deep Learning Neural Network for assessing the growth of green algae containing carotenoids - importance for environment, health and aquaculture.

Annals of agricultural and environmental medicine : AAEM
Using deep learning and neural networks enables us to greatly speed-up quantitative studies and provide a useful tool for analyzing microscopic images. Studies conducted on selected algae and sp. confirm the feasibility of using the deep learning n...

BSA-Seg: A Bi-level sparse attention network combining narrow band loss for multi-target medical image segmentation.

Neural networks : the official journal of the International Neural Network Society
Segmentation of multiple targets of varying sizes within medical images is of significant importance for the diagnosis of disease and pathological research. Transformer-based methods are emerging in the medical image segmentation, leveraging the powe...

The role of artificial intelligence in intraoral scanning for complete-arch digital impressions: An in vitro study.

Journal of dentistry
OBJECTIVES: Artificial intelligence (AI) is increasingly being integrated into intraoral scanners (IOS) to improve the quality of digital impressions. However, information on the accuracy of AI-assisted virtual models is limited. This study aimed to ...

Development and validation of pan-cancer lesion segmentation AI-model for whole-body 18F-FDG PET/CT in diverse clinical cohorts.

Computers in biology and medicine
BACKGROUND: This study develops a deep learning-based automated lesion segmentation model for whole-body 3DF-fluorodeoxyglucose (FDG)-Position emission tomography (PET) with computed tomography (CT) images agnostic to disease location and site.

A lightweight PCT-Net for segmenting neural fibers in low-quality CCM images.

Computers in biology and medicine
In this paper, we propose a lightweight Position Channel Transformer Network (PCT-Net) for segmenting slender neural fibers in low-quality corneal confocal microscopy images with speckle noise and uneven lighting. Three modules including the channel ...

Infection and Inflammation in Nuclear Medicine Imaging: The Role of Artificial Intelligence.

Seminars in nuclear medicine
Infectious and inflammatory diseases represent a global challenge. Delayed diagnosis and treatment lead to death, disabilities and impairment of the quality of life. The detection of low-grade inflammation and occult infections remains challenging. N...

Enhancing interdisciplinary image segmentation through a Gaussian-based modified local consensus spatial fuzzy approach.

Computers in biology and medicine
This study aims to introduce a generic fuzzy-based approach tailored explicitly for classifying images originating from an array of diverse sources, having varying degrees of spectral and spatial resolutions, inhomogeneity, artifacts, and entirely di...

A novel framework for segmentation of small targets in medical images.

Scientific reports
Medical image segmentation represents a pivotal and intricate procedure in the domain of medical image processing and analysis. With the progression of artificial intelligence in recent years, the utilization of deep learning techniques for medical i...

AAPMatcher: Adaptive attention pruning matcher for accurate local feature matching.

Neural networks : the official journal of the International Neural Network Society
Local feature matching, which seeks to establish correspondences between two images, serves as a fundamental component in numerous computer vision applications, such as camera tracking and 3D mapping. Recently, Transformer has demonstrated remarkable...