564 datasets found

Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis,...

A dataset containing 616 ICAs obtained from 210 patients used for training and testing the U-Net 3+ model for automatic extraction of coronary arteries.

Three-dimensional cine-MRI dataset for cardiac segmentation and classification

The dataset used in this paper is a set of 20 well-known deep-learning models, including AlexNet, ResNet, VGG, DenseNet, etc.

The dataset used in this paper is the BSDS500 dataset, which contains 200 natural images with over 1000 ground truth labellings.

The dataset used in the paper is the MobileNetV2 dataset, which is a pre-trained deep neural network model.

Fast R-CNN is a clean and fast update to R-CNN and SPPnet. It uses a single-stage training algorithm that jointly learns to classify object proposals and refine their spatial...

Chest radiography is one of the most ubiquitous diagnostic modalities for cardiothoracic and pulmonary abnormalities in the clinical setting. A timely diagnostic based on the...

Visual restoration and recognition are traditionally addressed in pipeline fashion, i.e. denoising followed by classification. Instead, observing correlations between the two...

The dataset used in this paper is a collection of state-action pairs generated by a pre-trained RL agent, used to train a self-supervised interpretable network (SSINet) to...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the CelebA-HQ, ImageNet, and Place2 datasets.

The dataset used in the paper is a ResNet trained on various datasets, including MNIST, Fashion MNIST, CIFAR10, STL10, and CIFAR100.

The dataset used in the paper is a synthetic dataset consisting of points in 2D that follow a spiral distribution.

A CT scan dataset about COVID-19

COVID-19 classification from CT chest images based on vision transformer models

This dataset contains images of cats and dogs, which is used for training deep neural networks.

A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries

Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies.

Vehicle logo recognition using Swin Transformer

Vehicle logo recognition using Swin Transformer