236 datasets found

The dataset used in the paper is CheXplain, a system that enables physicians to explore and understand data-driven, AI-enabled medical imaging analysis.

The dataset contains pre-operative scans of the TCGA-LGG collection.

A large head and neck CT dataset with 699 images from different patients with head and neck cancers, which were scanned during September 2015 and October 2019.

The dataset used for the proposed Concept-Centric Visual Turing Test (VTT) framework for evaluating MIC methods.

The REFLACX dataset contains reports and eye-tracking data for localization of abnormalities in chest x-rays.

A collection of laparoscopic and endoscopic video datasets for research purposes.

Deep learning-based dataset for computer-aided detection of medical images

BraTS 2018, 2019, 2020 datasets are used in the experiments.

A large-scale CT-derived µ-map dataset for training and evaluation of low-count PET attenuation map generation

A population-prior generation machine for generating population prior information for low-count PET attenuation map generation

Chest ImaGenome dataset

Federated Alternate Training (FAT) framework leverages unannotated data silos in federated segmentation for medical imaging.

The proposed model uses two publicly available annotated COVID-19 CT image datasets: COVID-19 CT provided by Zhao et al. (Zhao et al., 2020) and a dataset provided by the...

BloodMNIST is a medical imaging dataset containing 9,000 images of blood samples, each with a size of 224x224x3.

RetinaMNIST is a medical imaging dataset containing 5,000 images of retinal scans, each with a size of 224x224x3.

DermaMNIST is a medical imaging dataset containing 7,000 images of skin lesions, each with a size of 224x224x3.

PathMNIST is a medical imaging dataset containing 9,000 images of breast cancer tissue, each with a size of 224x224x3.

The dataset used in this paper is a collection of reconstructed T2-weighted MRI scans of fetuses, as well as T1- and T2-weighted MRI scans of 40 newborns.

The dataset used in this paper is a collection of reconstructed T2-weighted MRI scans of fetuses, as well as T1- and T2-weighted MRI scans of 40 newborns.

The ONSD dataset contains ultrasound videos of optic nerve sheath diameter measurements.