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This study introduces a novel method for capturing and digitally rendering the color appearance of physical hair samples using deep neural networks.

This study introduces a novel method for digitizing hair color that accurately captures and renders the color appearance of physical hair samples in synthetic images.

This study found that self-supervised pretraining significantly improves accuracy in medical image classifiers for dermatology and chest X-ray tasks, outperforming supervised baselines and showing robustness to distribution shifts with limited labeled data.

This study reports that deep learning models, particularly CNN and FCN, achieved high accuracy in diagnosing scalp and skin disorders, suggesting potential for improved diagnostic systems with further advancements.

In this study, a machine learning framework incorporating the CatBoost algorithm accurately predicted Systemic Lupus Erythematosus in Omani patients, suggesting potential for early clinical intervention.