Translational vision science & technology
May 2, 2022
PURPOSE: To develop a three-dimensional (3D) deep learning algorithm to detect glaucoma using spectral-domain optical coherence tomography (SD-OCT) optic nerve head (ONH) cube scans and validate its performance on ethnically diverse real-world datase...
SIGNIFICANCE: The Monte Carlo (MC) method is widely used as the gold-standard for modeling light propagation inside turbid media, such as human tissues, but combating its inherent stochastic noise requires one to simulate a large number photons, resu...
SIGNIFICANCE: The imaging of objects hidden in light-scattering media is a vital practical task in a wide range of applications, including biological imaging. Deep-learning-based methods have been used to reconstruct images behind scattering media un...
BACKGROUND: Peritoneal recurrence is the predominant pattern of relapse after curative-intent surgery for gastric cancer and portends a dismal prognosis. Accurate individualised prediction of peritoneal recurrence is crucial to identify patients who ...
RATIONALE AND OBJECTIVES: Our goal was to create an artificial intelligence (AI) training curriculum for residents that taught them to create, train, evaluate and refine deep learning (DL) models. Hands-on training of models was emphasized and didact...
Cardiovascular disease is both a risk factor and potential outcome of the direct, indirect and long-term effects of COVID-19. A recent analysis in >150,000 survivors of COVID-19 demonstrates an increased 1-year risk of numerous cardiovascular disease...
Nature reviews. Molecular cell biology
May 1, 2022
Deep learning has tremendous potential in single-cell data analyses, but numerous challenges and possible new developments remain to be explored. In this commentary, we consider the progress, limitations, best practices and outlook of adapting deep l...
OBJECTIVES: Renal cell carcinoma (RCC) is often found incidentally in asymptomatic individuals undergoing abdominal computed tomography (CT) examinations. The purpose of our study is to develop a deep learning-based algorithm for fully automated dete...
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