766 datasets found

ISLES 2017 dataset is used for medical image segmentation

An anonymized internal trial dataset for vertebrae segmentation and identification from spinal X-ray images

The dataset used for the AutoPET 2022 challenge contains 1014 cases.

Diabetic Retinopathy dataset containing 1000 color fundus images from KAGGLE

Mammogram classification is directly related to computer-aided di-agnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to...

Two public low-dose CT denoising datasets

Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising

Spitzoid lesions dataset used for diagnosis of Spitzoid lesions using GA feature selection and Random Forest

Malaria cell image classification dataset

The FLARE2021 dataset is a large-scale dataset for abdominal multi-organ segmentation. It contains CT scans of various organs and includes cases with lesions and other...

Residual and plain convolutional neural networks for 3d brain mri classification

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

Breast Cancer images dataset

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based...

A publicly available dataset containing chest X-ray images of 3616 coronavirus disease (COVID-19), 10192 healthy, 6012 lung opacity, and 1340 Pneumonia

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

MRI dataset for hippocampus segmentation

The dataset used for testing and validation of the proposed Monte Carlo Transformer-based U-Net (MC-Swin-U) architecture for tumor and OAR segmentation in radiation oncology...

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

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based...