AIMC Topic: Retina

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Spike-VisNet: A novel framework for visual recognition with FocusLayer-STDP learning.

Neural networks : the official journal of the International Neural Network Society
Current vision-inspired spiking neural networks (SNNs) face key challenges due to their model structures typically focusing on single mechanisms and neglecting the integration of multiple biological features. These limitations, coupled with limited s...

Computer-aided diagnosis of early-stage Retinopathy of Prematurity in neonatal fundus images using artificial intelligence.

Biomedical physics & engineering express
Retinopathy of Prematurity (ROP) is a retinal disorder affecting preterm babies, which can lead to permanent blindness without treatment. Early-stage ROP diagnosis is vital in providing optimal therapy for the neonates. The proposed study predicts ea...

TSOM: Small object motion detection neural network inspired by avian visual circuit.

Neural networks : the official journal of the International Neural Network Society
Detecting small moving objects in complex backgrounds from an overhead perspective is a highly challenging task for machine vision systems. As an inspiration from nature, the avian visual system is capable of processing motion information in various ...

Artificial intelligence-based prediction of neurocardiovascular risk score from retinal swept-source optical coherence tomography-angiography.

Scientific reports
The recent rise of artificial intelligence represents a revolutionary way of improving current medical practices, including cardiovascular (CV) assessment scores. Retinal vascular alterations may reflect systemic processes such as the presence of CV ...

Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions.

Scientific reports
Self-supervised learning has become the cornerstone of building generalizable and transferable artificial intelligence systems in medical imaging. In particular, contrastive representation learning techniques trained on large multi-modal datasets hav...

Self-supervised based clustering for retinal optical coherence tomography images.

Eye (London, England)
BACKGROUND: In response to the inadequacy of manual analysis in meeting the rising demand for retinal optical coherence tomography (OCT) images, a self-supervised learning-based clustering model was implemented.

Grading of diabetic retinopathy using a pre-segmenting deep learning classification model: Validation of an automated algorithm.

Acta ophthalmologica
PURPOSE: To validate the performance of autonomous diabetic retinopathy (DR) grading by comparing a human grader and a self-developed deep-learning (DL) algorithm with gold-standard evaluation.

Privacy-Preserving Technology Using Federated Learning and Blockchain in Protecting against Adversarial Attacks for Retinal Imaging.

Ophthalmology
PURPOSE: Collaboration provides valuable data for robust artificial intelligence (AI) model development. Federated learning (FL) is a privacy-enhancing technology that allows collaboration while respecting privacy via the development of models withou...

Artificial intelligence-based analysis of retinal fluid volume dynamics in neovascular age-related macular degeneration and association with vision and atrophy.

Eye (London, England)
BACKGROUND/OBJECTIVES: To characterise morphological changes in neovascular age-related macular degeneration (nAMD) during anti-angiogenic therapy and explore relationships with best-corrected visual acuity (BCVA) and development of macular atrophy (...

Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study.

The lancet. Healthy longevity
BACKGROUND: Biological ageing markers are useful to risk stratify morbidity and mortality more precisely than chronological age. In this study, we aimed to develop a novel deep-learning-based biological ageing marker (referred to as RetiPhenoAge here...