Motivation
- To bridge the evident gap between hospitals and industry/research
- Here we list all the opensource biomedical databases, which can be used for research
Datasets
BraTS, 2018.
S. Bakas et.al(about) (get data) (DOI)
All BraTS multimodal scans are available as NIfTI files (.nii.gz) and describe a) native (T1) and b) post-contrast T1-weighted (T1Gd), c) T2-weighted (T2), and d) T2 Fluid Attenuated Inversion Recovery (T2-FLAIR) volumes, and were acquired with differentclinical protocols and various scanners from multiple (n=19) institutions, mentioned as data contributors here. All the imaging datasets have been segmented manually, by one to four raters, following the same annotation protocol, and their annotations were approved by experienced neuro-radiologists. Annotations comprise the GD-enhancing tumor (ET — label 4), the peritumoral edema (ED — label 2), and the necrotic and non-enhancing tumor core (NCR/NET — label 1), as described both in the BraTS 2012-2013 TMI paper and in the latest BraTS summarizing paper (also see Fig.1). The provided data are distributed after their pre-processing, i.e. co-registered to the same anatomical template, interpolated to the same resolution (1 mm^3) and skull-stripped.
10.1109/TMI.2014.2377694, 10.1038/sdata.2017.117, arXiv:1811.02629 (2018)
Diabetic Retinopathy, 2015.
California Healthcare Foundation(about) (get data)
The US Center for Disease Control and Prevention estimates that 29.1 million people in the US have diabetes and the World Health Organization estimates that 347 million people have the disease worldwide. Diabetic Retinopathy (DR) is an eye disease associated with long-standing diabetes. Around 40% to 45% of Americans with diabetes have some stage of the disease. Progression to vision impairment can be slowed or averted if DR is detected in time, however this can be difficult as the disease often shows few symptoms until it is too late to provide effective treatment. Currently, detecting DR is a time-consuming and manual process that requires a trained clinician to examine and evaluate digital color fundus photographs of the retina. By the time human readers submit their reviews, often a day or two later, the delayed results lead to lost follow up, miscommunication, and delayed treatment. Clinicians can identify DR by the presence of lesions associated with the vascular abnormalities caused by the disease. While this approach is effective, its resource demands are high. The expertise and equipment required are often lacking in areas where the rate of diabetes in local populations is high and DR detection is most needed. As the number of individuals with diabetes continues to grow, the infrastructure needed to prevent blindness due to DR will become even more insufficient. The need for a comprehensive and automated method of DR screening has long been recognized, and previous efforts have made good progress using image classification, pattern recognition, and machine learning. With color fundus photography as input, the goal of this competition is to push an automated detection system to the limit of what is possible – ideally resulting in models with realistic clinical potential. The winning models will be open sourced to maximize the impact such a model can have on improving DR detection.
MURA, 2018.
Pranav Rajpurkar, et.al(about) (get data) (DOI)
MURA (musculoskeletal radiographs) is a large dataset of bone X-rays. Algorithms are tasked with determining whether an X-ray study is normal or abnormal. Musculoskeletal conditions affect more than 1.7 billion people worldwide, and are the most common cause of severe, long-term pain and disability, with 30 million emergency department visits annually and increasing. We hope that our dataset can lead to significant advances in medical imaging technologies which can diagnose at the level of experts, towards improving healthcare access in parts of the world where access to skilled radiologists is limited. MURA is one of the largest public radiographic image datasets. We're making this dataset available to the community and hosting a competition to see if your models can perform as well as radiologists on the task.
arXiv:1712.06957
Chest Xray, 2017.
NIH Clinical Center(about) (get data) (DOI)
The NIH Clinical Center recently released over 100,000 anonymized chest x-ray images and their corresponding data to the scientific community. The release will allow researchers across the country and around the world to freely access the datasets and increase their ability to teach computers how to detect and diagnose disease. Ultimately, this artificial intelligence mechanism can lead to clinicians making better diagnostic decisions for patients. NIH compiled the dataset of scans from more than 30,000 patients, including many with advanced lung disease. Patients at the NIH Clinical Center, the nation’s largest hospital devoted entirely to clinical research, are partners in research and voluntarily enroll to participate in clinical trials. With patient privacy being paramount, the dataset was rigorously screened to remove all personally identifiable information before release. Reading and diagnosing chest x-ray images may be a relatively simple task for radiologists but, in fact, it is a complex reasoning problem which often requires careful observation and knowledge of anatomical principles, physiology and pathology. Such factors increase the difficulty of developing a consistent and automated technique for reading chest X-ray images while simultaneously considering all common thoracic diseases. By using this free dataset, the hope is that academic and research institutions across the country will be able to teach a computer to read and process extremely large amounts of scans, to confirm the results radiologists have found and potentially identify other findings that may have been overlooked. In addition, this advanced computer technology may also be able to, help identify slow changes occurring over the course of multiple chest x-rays that might otherwise be overlooked benefit patients in developing countries that do not have access to radiologists to read their chest x-rays, and create a virtual radiology resident that can later be taught to read more complex images like CT and MRI in the future. With an ongoing commitment to data sharing, the NIH research hospital anticipates adding a large dataset of CT scans to be made available as well in the coming months.
http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.pdf
ABIDE, 2017.
Di Martino et.al(about) (get data) (DOI)
Autism spectrum disorder (ASD) is characterized by qualitative impairment in social reciprocity, and by repetitive, restricted, and stereotyped behaviors/interests. Previously considered rare, ASD is now recognized to occur in more than 1% of children. Despite continuing research advances, their pace and clinical impact have not kept up with the urgency to identify ways of determining the diagnosis at earlier ages, selecting optimal treatments, and predicting outcomes. For the most part this is due to the complexity and heterogeneity of ASD. To face these challenges, large-scale samples are essential, but single laboratories cannot obtain sufficiently large datasets to reveal the brain mechanisms underlying ASD. In response, the Autism Brain Imaging Data Exchange (ABIDE) initiative has aggregated functional and structural brain imaging data collected from laboratories around the world to accelerate our understanding of the neural bases of autism. With the ultimate goal of facilitating discovery science and comparisons across samples, the ABIDE initiative now includes two large-scale collections, ABIDE I and ABIDE II. Each collection was created through the aggregation of datasets independently collected across more than 24 international brain imaging laboratories and are being made available to investigators throughout the world, consistent with open science principles, such as those at the core of the International Neuroimaging Data-sharing Initiative.
https://www.ncbi.nlm.nih.gov/pubmed/23774715
ADNI, 2010.
R. C. Peterson et.al(about) (get data) (DOI)
The Alzheimers Disease Neuroimaging Initiative (ADNI) is a longitudinal multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimers disease (AD). Since its launch more than a decade ago, the landmark public-private partnership has made major contributions to AD research, enabling the sharing of data between researchers around the world. Three overarching goals of the ADNI study are (1) To detect AD at the earliest possible stage (pre-dementia) and identify ways to track the disease's progression with biomarkers. (2) To support advances in AD intervention, prevention, and treatment through the application of new diagnostic methods at the earliest possible stages (when intervention may be most effective). (3) To continually administer ADNI's innovative data-access policy, which provides all data without embargo to all scientists in the world.
https://n.neurology.org/content/74/3/201.short
Camelyon, 2017.
Geert Litjens et.al(about) (get data) (DOI)
The data in this challenge contains whole-slide images (WSI) of hematoxylin and eosin (H and E) stained lymph node sections. Depending on the particular data set (see below), ground truth is provided- On a lesion-level - with detailed annotations of metastases in WSI. On a patient-level- with a pN-stage label per patient. All ground truth annotations were carefully prepared under supervision of expert pathologists. For the purpose of revising the slides, additional slides stained with cytokeratin immunohistochemistry were used. If however, you encounter problems with the data set, then please report your findings at the forum. The data set for CAMELYON17 is collected from 5 medical centres in the Netherlands. WSI are provided as TIFF images. Lesion-level annotations are provided as XML files. For training, 100 patients will be provided and another 100 patients for testing. This means we will release 1000 slides with 5 slides per patient.
10.1093/gigascience/giy065, 10.1001/jama.2017.14585, 10.1109/TMI.2018.2867350