The convolutional neural network (CNN) is a potent and popular neural network types and has been crucial to deep learning in recent years. A standard CNN which is known as 2-dimensions CNN was first ...

This paper proposes an end-to-end trained fully convolutional neural network model to process 3D image volumes. Unlike previous works that processed the input volumes slice-wise or patch-wise, the ...

As HSI that with 2D spatial and 1D spectral information is quite different from 3D object image, the existing DNN cannot be directly extended to hyperspectral image (HSI) classification. A Multiscale ...

Machine learning is successful in many imaging applications, such as image classification (1–3) and semantic segmentation (4–6).Many applications of machine learning to imaging problems use deep ...

A PyTorch-based library for working with 3D and 2D convolutional neural networks, with focus on semantic segmentation of volumetric biomedical image data. Quick overview of elektronn3's code structure ...

This project implements a 3D Convolutional Neural Network (CNN) accelerator using Verilog. The accelerator processes 3D image and filter data to perform convolution, max pooling, and feedforward ...