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Knee MRI dataset
MRI reconstruction dataset for unpaired training -
Deep-Learning-liver-segmentation
A public dataset, available in NifTi format, for automatic liver segmentation. -
COVID-19 Detection Dataset
The dataset used in this study for COVID-19 detection, containing laboratory parameters and chest x-ray images. -
Medical Knowledge-Guided Deep Curriculum Learning for Elbow Fracture Diagnosi...
Elbow fracture diagnosis from X-ray images using medical knowledge-guided deep curriculum learning -
COVID-19 CT scans and corresponding reports
COVID-19 CT scans and corresponding reports from a university hospital -
Breast Cancer Wisconsin (Diagnostics) dataset
The dataset used in this paper is the Breast Cancer Wisconsin (Diagnostics) dataset, which contains cytological characteristics of 699 breast fine-needle aspirates. -
Chest X-ray
Chest X-ray dataset is a medical image dataset containing 5,856 images of chest X-rays. -
CheXpert: A large chest radiograph dataset with uncertainty labels and expert...
Large chest radiograph dataset with uncertainty labels and expert comparison. -
Chest X-ray datasets
Chest X-ray datasets used to train machine learning models, potentially suffering from population, prevalence, and annotation shift biases. -
Dixon MRI dataset for skeletal muscle segmentation
A dataset comprising 10 out-of-phase 3pt Dixon MRI volumes was used for algorithm development and testing. -
Glioma Growth Dataset
An in-house dataset containing a total of 199 longitudinal MRI scans from 38 patients suffering from glioma -
BraTS 2019 validation and testing datasets
The BraTS 2019 validation and testing datasets are used to evaluate the performance of the proposed segmentation method. -
BraTS 2019 training dataset
Multimodal brain tumor segmentation challenge (BraTS) aims to evaluate state-of-the-art methods for the segmentation of brain tumors by providing a 3D MRI dataset with ground... -
Radnet: Radiologist Level Accuracy Using Deep Learning for Hemorrhage Detecti...
A dataset of studies tagged slice-wise by radiologists for training a deep learning algorithm for detection of hemorrhage in CT scans. -
Deep 3D Convolution Neural Network for CT Brain Hemorrhage Classification
A dataset of 40k studies assembled for training a 3D convolution neural network for CT brain hemorrhage classification. -
Improved ICH Classification Using Task-Dependent Learning
BloodNet is a deep learning architecture designed for optimal triaging of Head CTs, with the goal of decreasing the time from CT acquisition to accurate ICH detection. -
Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling
Processed two public datasets to set up a benchmark cohort of 341 patient for studying outcome prediction of multifocal metastatic cancer. -
HSI dataset for RGB image-to-StO2 translation
The dataset used for training and testing the Conditional Generative Adversarial Networks (cGAN) for estimating tissue oxygen saturation from RGB images.
