In the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Given the micrometer resolution of OCT systems, consecutive images are oft...
Artisanal and small-scale mines (asm) are on the rise. They represent a crucial source of wealth for numerous communities but are rarely monitored or regulated. The main reason being the unavailability of reliable information on the precise location ...
AJR. American journal of roentgenology
Sep 21, 2022
Iterative reconstruction (IR) techniques are susceptible to contrast-dependent spatial resolution, limiting overall radiation dose reduction potential. Deep learning image reconstruction (DLIR) may mitigate this limitation. The purpose of our study...
PURPOSE: To develop a physics-guided deep learning (PG-DL) reconstruction strategy based on a signal intensity informed multi-coil (SIIM) encoding operator for highly-accelerated simultaneous multislice (SMS) myocardial perfusion cardiac MRI (CMR).
PURPOSE: To develop a clinical CEST MR fingerprinting (CEST-MRF) method for brain tumor quantification using EPI acquisition and deep learning reconstruction.
Blastoid/pleomorphic morphology is associated with short survival in mantle cell lymphoma (MCL), but its prognostic value is overridden by Ki-67 in multivariate analysis. Herein, a nuclear segmentation model was developed using deep learning, and nuc...
Experimental biology and medicine (Maywood, N.J.)
Sep 21, 2022
There is growing interest in imputing missing data in tabular datasets using deep learning. Existing deep learning-based imputation models have been commonly evaluated using root mean square error (RMSE) as the predictive accuracy metric. In this art...
An important challenge and limiting factor in deep learning methods for medical imaging segmentation is the lack of available of annotated data to properly train models. For the specific task of tumor segmentation, the process entails clinicians labe...
Critical reviews in oncology/hematology
Sep 21, 2022
Radiomics and deep learning (DL) hold transformative promise and substantial and significant advances in oncology; however, most methods have been tested in retrospective or simulated settings. There is considerable interest in the biomarker validati...
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