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RML2016.10a
The dataset used in this paper for Automatic Modulation Classification (AMC) using deep learning. -
CICIDS2017
The CICIDS2017 dataset was collected 2 years after the UNSW NB-15 data. The collection took place for a week. On each day attacks such as: Brute Force FTP, Brute Force SSH, DoS,... -
CLINIC-metal
CLINIC-metal dataset for testing metal artifact reduction methods. -
Synthesized Dental
Synthesized Dental dataset for testing metal artifact reduction methods. -
ChestX-ray8
A hospital-scale chest X-ray database, namely “ChestX-ray8”, which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight common disease... -
The Cancer Imaging Archive (TCIA)
The dataset used in this study is from the Brain Tumor Radiogenomic Classification challenge (Baid et al., 2021) that includes multi-parametric MRI (mpMRI) scans for 585... -
Brain Tumor Radiogenomic Classification challenge
The dataset used in this study is from the Brain Tumor Radiogenomic Classification challenge (Baid et al., 2021) that includes multi-parametric MRI (mpMRI) scans for 585... -
Automatic detection and counting of wheat spikelet
A dataset used for automatic detection and counting of wheat spikelet using semi-automatic labeling and deep learning. -
Implicit Multigrid-Augmented DL for the Helmholtz Equation
The dataset used in this paper is a collection of slowness models for the Helmholtz equation, generated from the CIFAR-10, OpenFWI Style-A, and STL-10 datasets. -
Low-dose CT image denoising dataset
The dataset used for training and testing deep neural networks-based denoising models for CT imaging. -
FaultSeg3D
A dataset for automatic fault recognition from 3D seismic datasets. -
Controlled Latent Space Sampling for Antimicrobial Peptide Design
A dataset of peptide sequences used for training and testing the proposed CLaSS method for controlled generation of antimicrobial peptides. -
ResNet18 dataset
The dataset used in the paper is the ResNet18 dataset, which is a convolutional neural network dataset. -
COVID-19 detection using chest X-rays: is lung segmentation important for gen...
A large DNN, containing 3 stacked modules. The segmentation module is an U-Net, trained beforehand to receive X-rays and output segmentation masks. -
Deep Learning in Optical Metrology
Deep learning in optical metrology -
Deep residual learning for image recognition
The ResNet-50 and ResNet-101 are used as the backbone image feature extractor.
