AIMC Topic: Image Processing, Computer-Assisted

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Enhancing YOLOv8n with Mamba-like linear attention for defect detection and coating thickness analysis of irregular film tablet.

International journal of pharmaceutics
This study presents a real-time system that integrates deep learning and machine vision for defect detection and coating thickness measurement of irregularly shaped film-coated tablets. To overcome the accuracy and speed limitations of the traditiona...

Development of an AI model for DILI-level prediction using liver organoid brightfield images.

Communications biology
AI image processing techniques hold promise for clinical applications by enabling analysis of complex status information from cells. Importantly, real-time brightfield imaging has advantages of informativeness, non-destructive nature, and low cost ov...

Unsupervised Adaptive Deep Learning Framework for Video Denoising in Light Scattering Imaging.

Analytical chemistry
Light scattering is a powerful tool that has been widely applied in various scenarios, such as nanoparticle analysis, single-cell measurement, and blood flow monitoring. However, noise is always a concerning and challenging issue in light scattering ...

AI Quantification of Vascular Lesions in Mouse Fundus Fluorescein Angiography.

Translational vision science & technology
PURPOSE: Quantifying vascular leakage in fundus fluorescein angiography (FFA) is a critical endpoint in preclinical models of diseases such as neovascular age-related macular degeneration, retinopathy of prematurity, and diabetic retinopathy. Traditi...

Multi-Objective Evolutionary Optimization Boosted Deep Neural Networks for Few-Shot Medical Segmentation With Noisy Labels.

IEEE journal of biomedical and health informatics
Fully-supervised deep neural networks have achieved remarkable progress in medical image segmentation, yet they heavily rely on extensive manually labeled data and exhibit inflexibility for unseen tasks. Few-shot segmentation (FSS) addresses these is...

Ensemble learning of deep CNN models and two stage level prediction of Cobb angle on surface topography in adolescents with idiopathic scoliosis.

Medical engineering & physics
This study employs Convolutional Neural Networks (CNNs) as feature extractors with appended regression layers for the non-invasive prediction of Cobb Angle (CA) from Surface Topography (ST) scans in adolescents with Idiopathic Scoliosis (AIS). The ai...

Optimised Hybrid Attention-Based Capsule Network Integrated Three-Pathway Network for Chronic Disease Detection in Retinal Images.

Journal of evaluation in clinical practice
BACKGROUND: Over the past 20 years, researchers have concentrated on generating retinal images as a means of detecting and classifying chronic diseases. Early diagnosis and treatment are essential to avoid chronic diseases. Manually grading retinal i...

A novel dose calculation system implemented in image domain.

Medical physics
PURPOSE: Modern intensity-modulated radiotherapy, aiming to deliver an accurate dose to the planning target volume while protecting the surrounding organs at risk, is regarded as the indispensable treatment for cancer in the clinic. An accurate and e...

Implementing 100% quality control in a cervical cytology workflow using whole slide images and artificial intelligence provided by the Techcyte SureView™ System.

Cancer cytopathology
BACKGROUND: Recent advancements in digital pathology have extended into cytopathology. Laboratories screening cervical cytology specimens now choose between limited imaging options and traditional manual microscopy. The Techcyte SureView™ Cervical Cy...

Exploring interpretable echo analysis using self-supervised parcels.

Computers in biology and medicine
The application of AI for predicting critical heart failure endpoints using echocardiography is a promising avenue to improve patient care and treatment planning. However, fully supervised training of deep learning models in medical imaging requires ...