Datasets
30 Datasets
cathy-stones/mimic-cxr
MIMIC-CXR from Massachusetts Institute of Technology presents 371,920 chest X-rays associated with 227,943 imaging studies from 65,079 patients. The studies were performed at Beth Israel Deaconess Medical Center in Boston, MA.
cathy-stones/ham10000
HAM10000 is a dataset of 10000 training images for detecting pigmented skin lesions. The authors collected dermatoscopic images from different populations, acquired and stored by different modalities.
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cathy-stones/cord-19
CORD-19 is a free resource of tens of thousands of scholarly articles about COVID-19, SARS-CoV-2, and related coronaviruses for use by the global research community.
cathy-stones/kvasir-seg
Kvasir-SEG is an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an experienced gastroenterologist.
cathy-stones/stare
The STARE (Structured Analysis of the Retina) dataset is a dataset for retinal vessel segmentation. It contains 20 equal-sized (700×605) color fundus images. For each image, two groups of annotations are provided.
cathy-stones/luna
The LUNA challenges provide datasets for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set.
cathy-stones/cholec80
Cholec80 is an endoscopic video dataset containing 80 videos of cholecystectomy surgeries performed by 13 surgeons. The videos are captured at 25 fps and downsampled to 1 fps for processing.
cathy-stones/padchest
PadChest is a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports.
cathy-stones/glas
The dataset used in this challenge consists of 165 images derived from 16 H&E stained histological sections of stage T3 or T42 colorectal adenocarcinoma.
cathy-stones/sleep-edf
The sleep-edf database contains 197 whole-night PolySomnoGraphic sleep recordings, containing EEG, EOG, chin EMG, and event markers. Some records also contain respiration and body temperature.
cathy-stones/luna16
The LUNA16 (LUng Nodule Analysis) dataset is a dataset for lung segmentation. It consists of 1,186 lung nodules annotated in 888 CT scans.
cathy-stones/medical-segment
The Medical Segmentation Decathlon is a collection of medical image segmentation datasets. It contains a total of 2,633 three-dimensional images collected across multiple anatomies of interest, multiple modalities and multiple sources. Specifically, it contains data for the following body organs or parts: Brain, Heart, Liver, Hippocampus, Prostate, Lung, Pancreas, Hepatic Vessel, Spleen and Colon.
cathy-stones/pcam
The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections.
cathy-stones/chestx-ray8
ChestX-ray8 is a medical imaging dataset which comprises 108,948 frontal-view X-ray images of 32,717 (collected from the year of 1992 to 2015) unique patients with the text-mined eight common disease labels, mined from the text radiological reports via NLP techniques.
cathy-stones/brats-2017
The BRATS2017 dataset. It contains 285 brain tumor MRI scans, with four MRI modalities as T1, T1ce, T2, and Flair for each scan. The dataset also provides full masks for brain tumors, with labels for ED, ET, NET/NCR. The segmentation evaluation is based on three tasks: WT, TC and ET segmentation.
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cathy-stones/ppmi
The Parkinson’s Progression Markers Initiative (PPMI) dataset originates from an observational clinical and longitudinal study comprising evaluations of people with Parkinson’s disease (PD), those people with high risk, and those who are healthy.
cathy-stones/promise12
The PROMISE12 dataset was made available for the MICCAI 2012 prostate segmentation challenge. Magnetic Resonance (MR) images (T2-weighted) of 50 patients with various diseases were acquired at different locations with several MRI vendors and scanning protocols.
cathy-stones/vqa-rad
VQA-RAD consists of 3,515 question–answer pairs on 315 radiology images.
cathy-stones/brats-2015
The BraTS 2015 dataset is a dataset for brain tumor image segmentation. It consists of 220 high grade gliomas (HGG) and 54 low grade gliomas (LGG) MRIs. The four MRI modalities are T1, T1c, T2, and T2FLAIR. Segmented “ground truth” is provide about four intra-tumoral classes, viz. edema, enhancing tumor, non-enhancing tumor, and necrosis.
cathy-stones/camus
This project aims to provide all the materials to the community to resolve the problem of echocardiographic image segmentation and volume estimation from 2D ultrasound sequences (both two and four-chamber views).
cathy-stones/ubfc-rppg
A CMS50E transmissive pulse oximeter was used to obtain the ground truth PPG data comprising the PPG waveform as well as the PPG heart rates.
cathy-stones/consep
The colorectal nuclear segmentation and phenotypes (CoNSeP) dataset consists of 41 H&E stained image tiles, each of size 1,000×1,000 pixels at 40× objective magnification. The images were extracted from 16 colorectal adenocarcinoma (CRA) WSIs
cathy-stones/amos
A large-scale, diverse, clinical dataset for abdominal organ segmentation.
cathy-stones/chase-db1
CHASE_DB1 is a dataset for retinal vessel segmentation which contains 28 color retina images with the size of 999×960 pixels which are collected from both left and right eyes of 14 school children. Each image is annotated by two independent human experts.
cathy-stones/physionet-challenge-2012
cathy-stones/chexpert
A large dataset that contains 224,316 chest radiographs of 65,240 patients.
cathy-stones/drive
The Digital Retinal Images for Vessel Extraction (DRIVE) dataset is a dataset for retinal vessel segmentation.
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cathy-stones/chestx-ray14
ChestX-ray14 is a medical imaging dataset which comprises 112,120 frontal-view X-ray images of 30,805 (collected from the year of 1992 to 2015) unique patients with the text-mined fourteen common disease labels, mined from the text radiological reports via NLP techniques.
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cathy-stones/birl
The initial dataset of stained histological tissues is composed by image pairs of related sections (mainly, consecutive cuts). Each image in the pair is coloured with a different stain.
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cathy-stones/mimic-website
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