40 citations · 47 across the 6 of their papers we have counts for
6 papers
Distributed Conditional GAN (discGAN) For Synthetic Healthcare Data Generation
David Fuentes, Diana McSpadden, Sodiq Adewole
In this paper, we propose a distributed Generative Adversarial Networks (discGANs) to generate synthetic tabular data specific to the healthcare domain. While using GANs to generat…
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging Segmentation
Adrian Celaya, Beatrice Riviere, David Fuentes
Accurate medical imaging segmentation is critical for precise and effective medical interventions. However, despite the success of convolutional neural networks (CNNs) in medical i…
A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentation
Adrian Celaya, Beatrice Riviere, David Fuentes
Within medical imaging segmentation, the Dice coefficient and Hausdorff-based metrics are standard measures of success for deep learning models. However, modern loss functions for…
Mutual-Information Based Optimal Experimental Design for Hyperpolarized C-Pyruvate MRI
Prashant K. Jha, Christopher Walker, Drew Mitchell +4
A key parameter of interest recovered from hyperpolarized (HP) MRI measurements is the apparent pyruvate-to-lactate exchange rate, , for measuring tumor metabolism. This ma…
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy
Rajarajeswari Muthusivarajan, Adrian Celaya, Joshua P. Yung +4
Deep neural networks with multilevel connections process input data in complex ways to learn the information.A networks learning efficiency depends not only on the complex neural n…
PocketNet: A Smaller Neural Network for Medical Image Analysis
Adrian Celaya, Jonas A. Actor, Rajarajeswari Muthusivarajan +5
Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models. To address such issues, we propose the PocketNe…