236 datasets found

A simulated low-dose CT dataset generated from normal-dose CT images to be used for training a deep neural network to remove noise from low-dose CT images.

The dataset used in the paper is a collection of 1262 t1-weighted brain MRIs of subjects from three different datasets: the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1),...

COVID-19-20_v2

COVID-19 CT scans

COVID-19 CT images of the lungs, ground glass turbidity is the most common finding that requires specialist diagnosis.

The dataset used for training and testing the DeepMediX model, which consists of brain MRI and skin cancer images.

MoSt-DSA is a dataset for direct multi-frame interpolation in DSA images. It contains 470 head DSA image sequences from 8 hospitals, each containing 152 images of 489x489...

A hospital-scale chest x-ray dataset with weakly-supervised classification and localization of common thorax diseases

A large chest x-ray image dataset with multi-label annotated reports

The dataset consists of T1-weighted brain MRI images of adults, with manual segmentation of anatomical structures provided.

The BRACS dataset is a public dataset for whole slide image analysis, containing 1,000 whole slide images of breast cancer histopathology slides.

The CAMELYON-16 dataset is a public dataset for whole slide image analysis, containing 16,000 whole slide images of breast cancer histopathology slides.

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.

Hepatitis Patients dataset consists of 20 features and 155 observations

Liver Patients dataset consists of 583 observations and 11 features

Breast Cancer Wisconsin (Original) dataset consists of 699 observations and 11 features

A neural network with single-hidden layer of 64 hidden units and ReLU activations. A prior precision of ε = 1, a minibatch size of 128 and 16 Monte-Carlo samples are used for...

A publicly available dataset for cranial implant design, used for testing and evaluation of the automatic cranial implant design system.

The SIIM-ISIC Melanoma Classification Challenge dataset consists of dermoscopic images of histopathologically confirmed melanomas and benign melanoma mimickers.

Explainability is a longstanding challenge in deep learning, especially in high-stakes domains like healthcare. Common explainability methods highlight image regions that drive...