161 citations · 288 across the 26 of their papers we have counts for
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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…
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…