AIMC Topic: Photography

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Deep learning increases the availability of organism photographs taken by citizens in citizen science programs.

Scientific reports
Citizen science programs using organism photographs have become popular, but there are two problems related to photographs. One problem is the low quality of photographs. It is laborious to identify species in photographs taken outdoors because they ...

Joint Optimization of CycleGAN and CNN Classifier for Detection and Localization of Retinal Pathologies on Color Fundus Photographs.

IEEE journal of biomedical and health informatics
Retinal related diseases are the leading cause of vision loss, and severe retinal lesion causes irreversible damage to vision. Therefore, the automatic methods for retinal diseases detection based on medical images is essential for timely treatment. ...

Automated food intake tracking requires depth-refined semantic segmentation to rectify visual-volume discordance in long-term care homes.

Scientific reports
Malnutrition is a multidomain problem affecting 54% of older adults in long-term care (LTC). Monitoring nutritional intake in LTC is laborious and subjective, limiting clinical inference capabilities. Recent advances in automatic image-based food est...

DUNEScan: a web server for uncertainty estimation in skin cancer detection with deep neural networks.

Scientific reports
Recent years have seen a steep rise in the number of skin cancer detection applications. While modern advances in deep learning made possible reaching new heights in terms of classification accuracy, no publicly available skin cancer detection softwa...

Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans.

Scientific reports
Diabetes is a serious metabolic disorder with high rate of prevalence worldwide; the disease has the characteristics of improper secretion of insulin in pancreas that results in high glucose level in blood. The disease is also associated with other c...

DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.

Ophthalmology
PURPOSE: To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs.

Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods.

Computational and mathematical methods in medicine
One of the most common visual disorders is cataracts, which people suffer from as they get older. The creation of a cloud on the lens of our eyes is known as a cataract. Blurred vision, faded colors, and difficulty seeing in strong light are the main...

Newborn Eye Screening as an Application of AI.

Ophthalmic surgery, lasers & imaging retina
Artificial intelligence (AI) applications are diverse and serve varied functions in clinical practice. The most successful products today are clinical decision tools used by physicians, but autonomous AI is gaining traction. Widespread use of AI is l...

COVID surveillance robot: Monitoring social distancing constraints in indoor scenarios.

PloS one
Observing social/physical distancing norms between humans has become an indispensable precaution to slow down the transmission of COVID-19. We present a novel method to automatically detect pairs of humans in a crowded scenario who are not maintainin...