Application of a deep convolutional autoencoder network on MRI images of knees ... the images coming from different patients into 28800 2D black&white MRI images of size 64x64. The 3D dataset has a ...

This repository stores the code supporting my article- "3D Convolutional autoencoder for brain volumes: Learning spatial and temporal features of fMRI brain images". Concepts mentioned in the first ...

For example, Martinez-Murcia et al. (2020) extracted features from 3D brain MRI data of patients with Alzheimer’s dementia using a 3D convolutional autoencoder (3D-CAE). They demonstrated that the ...

This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI ... from each individual was reviewed using the ...

Abstract: The segmentation of brain tissue in MRI is valuable for extracting brain structure ... network models for medical image segmentation have addressed this using a 3D convolutional architecture ...

The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans. Fully connected upper layers of the 3D-CNN are then ...

The following is a summary of “Computer-aided diagnosis based on 3D deep convolutional neural network system using novel 3D magnetic resonance imaging sequences for high-grade prostate cancer,” ...