Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in par...
Deep neural networks (DNNs) have achieved high accuracy in diagnosing multiple diseases/conditions at a large scale. However, a number of concerns have been raised about safeguarding data privacy and algorithmic bias of the neural network models. We ...
This research introduces an innovative solution addressing the challenge of user authentication in cloud-based systems, emphasizing heightened security and privacy. The proposed system integrates multimodal biometrics, deep learning (Instance-based l...
BACKGROUND: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unst...
The integration of artificial intelligence (AI) in dermatology holds promise for enhancing clinical accuracy, enabling earlier detection of skin malignancies, suggesting potential management of skin lesions and eruptions, and promoting improved conti...
BACKGROUND: Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitat...
Deep Neural Networks (DNNs) based semantic segmentation of the robotic instruments and tissues can enhance the precision of surgical activities in robot-assisted surgery. However, in biological learning, DNNs cannot learn incremental tasks over time ...