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Found 7 papers, 4 papers with code
Solutions to Elliptic and Parabolic Problems via Finite Difference Based Unsupervised Small Linear Convolutional Neural Networks
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 equations (PDEs).
Joint inversion of Time-Lapse Surface Gravity and Seismic Data for Monitoring of 3D CO$_2$ Plumes via Deep Learning
We introduce a fully 3D, deep learning-based approach for the joint inversion of time-lapse surface gravity and seismic data for reconstructing subsurface density and velocity models.
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging Segmentation
Accurate medical imaging segmentation is critical for precise and effective medical interventions.
A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentation
Within medical imaging segmentation, the Dice coefficient and Hausdorff-based metrics are standard measures of success for deep learning models.
Inversion of Time-Lapse Surface Gravity Data for Detection of 3D CO$_2$ Plumes via Deep Learning
We introduce three algorithms that invert simulated gravity data to 3D subsurface rock/flow properties.
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy
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 network architecture but also on the input training images. Medical image segmentation with deep neural networks for skull stripping or tumor segmentation from magnetic resonance images enables learning both global and local features of the images. Though medical images are collected in a controlled environment, there may be artifacts or equipment based variance that cause inherent bias in the input set. In this study, we investigated the correlation between the image quality metrics of MR images with the neural network segmentation accuracy. For that we have used the 3D DenseNet architecture and let the network trained on the same input but applying different methodologies to select the training data set based on the IQM values. The difference in the segmentation accuracy between models based on the random training inputs with IQM based training inputs shed light on the role of image quality metrics on segmentation accuracy. By running the image quality metrics to choose the training inputs, further we may tune the learning efficiency of the network and the segmentation accuracy.
PocketNet: A Smaller Neural Network for Medical Image Analysis
Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models.
