1 citations · 1 across the 7 of their papers we have counts for
7 papers
Pre- and Post-Treatment Glioma Segmentation with the Medical Imaging Segmentation Toolkit
Adrian Celaya, Tucker Netherton, Dawid Schellingerhout +3
Medical image segmentation continues to advance rapidly, yet rigorous comparison between methods remains challenging due to a lack of standardized and customizable tooling. In this…
A Priori Generalizability Estimate for a CNN
Cito Balsells, Beatrice Riviere, David Fuentes
We formulate truncated singular value decompositions of entire convolutional neural networks. We demonstrate the computed left and right singular vectors are useful in identifying…
Learning Discontinuous Galerkin Solutions to Elliptic Problems via Small Linear Convolutional Neural Networks
Adrian Celaya, Yimo Wang, David Fuentes +1
In recent years, there has been an increasing interest in using deep learning and neural networks to tackle scientific problems, particularly in solving partial differential equati…
Adaptive Collocation Point Strategies For Physics Informed Neural Networks via the QR Discrete Empirical Interpolation Method
Adrian Celaya, David Fuentes, Beatrice Riviere
Physics-informed neural networks (PINNs) have gained significant attention for solving forward and inverse problems related to partial differential equations (PDEs). While advancem…
Deep Learning-Based Automated Post-Operative Gross Tumor Volume Segmentation in Glioblastoma Patients
Rajarajeswari Muthusivarajan, Adrian Celaya, Maguy Farhat +8
Precise automated delineation of post-operative gross tumor volume in glioblastoma cases is challenging and time-consuming owing to the presence of edema and the deformed brain tis…
MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Framework
Adrian Celaya, Evan Lim, Rachel Glenn +7
Medical imaging segmentation is a highly active area of research, with deep learning-based methods achieving state-of-the-art results in several benchmarks. However, the lack of st…
Solutions to Elliptic and Parabolic Problems via Finite Difference Based Unsupervised Small Linear Convolutional Neural Networks
Adrian Celaya, Keegan Kirk, David Fuentes +1
In recent years, there has been a growing interest in leveraging deep learning and neural networks to address scientific problems, particularly in solving partial differential equa…