60 datasets found

A CT scan dataset about COVID-19

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

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

The dataset is composed of a consecutive series of patients admitted to our hospital with a confirmed diagnosis of aneurysmal subarachnoid hemorrhage (SAH) between 2016 and 2022.

Liver steatosis segmentation dataset with deep learning methods

A successive subspace learning model, termed VoxelHop, for accurate classification of Amyotrophic Lateral Sclerosis (ALS) using T2-weighted structural MRI data.

The fastMRI AXT1 multi-coil brain dataset is consisted of 248 training volumes (3844 slices) and 82 validation (1424 slices) validation volumes.

The dataset used for knee menisci segmentation and relaxometry in 3D UTE cones MR imaging using attention U-Net with transfer learning

The proposed architecture uses a CNN-based approach to detect COVID-19 from chest X-ray images. The dataset is used to train and evaluate the proposed model.

COVID-19 CT dataset for predicting COVID-19 from chest CT images using attentional convolutional network.

Chest X-ray images dataset used for COVID-19 severity classification

A small dataset of 65 panoramic images for automatic detection of atherosclerotic carotid plaques.

The dataset used in this work consists of 9 axial volumes from Radiopaedia consisting of both positive and negative COVID indications.

The dataset used in this work consists of 20 volumes from Jun et al. consisting of infections labelled by two radiologists and verified by another experienced radiologist.

The dataset used in this work consists of 29 CT volumes from two different sources of lung infection data, resulting from COVID-19.

Ultrasound Localization Microscopy can resolve the microvascular bed down to a few micrometers. To achieve such performance microbubble contrast agents must perfuse the entire...

The ActualMed COVID-19 Chest X-ray Dataset Initiative is a collection of 13,975 CXR images of around 13,870 infected patients.

The COVID-19 Chest X-Ray Dataset Initiative is a collection of 13,975 CXR images of around 13,870 infected patients.

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...

The dataset used to train the proposed model is a combination of two open-source datasets called COVID-19 image data collection and Covid-Net.