26 datasets found

Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions.

Sparse adversarial attacks fool deep neural networks (DNNs) through minimal pixel perturbations, often regularized by the ℓ0 norm. Recent efforts have replaced this norm with a...

Machine learning and deep learning methods for cybersecurity

Fetal brain MRI dataset for automatic linear measurements of the fetal brain

A dataset used for automatic detection and counting of wheat spikelet using semi-automatic labeling and deep learning.

The dataset used for training and testing deep neural networks-based denoising models for CT imaging.

The dataset used in the paper is the ResNet18 dataset, which is a convolutional neural network dataset.

A large DNN, containing 3 stacked modules. The segmentation module is an U-Net, trained beforehand to receive X-rays and output segmentation masks.

A dataset for road damage detection and classification using deep neural networks with smartphone images.

This paper presents DADLNet, a dynamic domain adaptation based deep learning network framework for decoding motor imagery tasks.

The proposed scheme uses IoT based health monitoring and CNN based object detection methods to detect social distancing violations and track exposed or infected people.

The dataset of chest x-rays used in this paper come from COVID-19 image data collection from Joseph Paul Cohen and Paul Morrison and Lan Dao. This dataset contains x-rays of...

Rayleigh wave dispersion curves have been widely used in near-surface studies, and are primarily inverted for the shear wave (S-wave) velocity profiles. However, the inverse...

Lightweight vision transformer for US segmentation.

The dataset used in this paper is a serial Turbo Autoencoder (TurboAE) with Gaussian priors (TGP) for component training.

A dataset of 70 tampered and 30 authentic CT scans for evaluation of CT-GAN attack

The COVIDx dataset is a combination of many publicly available datasets for COVID-19 image classification.

FFHQ-UV: a large-scale 3D Morphable Models (3DMMs)-based facial UV-texture dataset derived from a natural image dataset FFHQ.

The authors used MNIST, SVHN, and CIFAR10 datasets for their experiments.

Local T2-FLAIR dataset used for testing the performance of the proposed Brain Slice Classification Algorithm (BSCA).