Medical Data for Machine Learning
This is a curated list of medical data for machine learning.
This list is provided for informational purposes only, please make sure you respect any and all usage restrictions for any of the data listed here.
1. Medical Imaging Data
The National Library of Medicine presents MedPix®
Database of 53,000 medical images from 13,000 patients with annotations. Requires registration.
ABIDE: The Autism Brain Imaging Data Exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism.
Function MRI images for 539 individuals suffering from ASD and 573 typical controls. These 1112 datasets are composed of structural and resting state functional MRI data along with an extensive array of phenotypic information. Requires registration.
Preprocessed version: http://preprocessed-connectomes-project.org/abide/
Alzheimer's Disease Neuroimaging Initiative (ADNI)
MRI database on Alzheimer's patients and healthy controls. Also has clinical, genomic, and biomaker data. Requires registration.
Digital Retinal Images for Vessel Extraction (DRIVE)
The DRIVE database is for comparative studies on segmentation of blood vessels in retinal images. It consists of 40 photographs out of which 7 showing signs of mild early diabetic retinopathy.
AMRG Cardiac Atlas The AMRG Cardiac MRI Atlas is a complete labelled MRI image set of a normal patient's heart acquired with the Auckland MRI Research Group 's Siemens Avanto scanner. The atlas aims to provide university and school students, MR technologists, clinicians...
Congenital Heart Disease (CHD) Atlas The Congenital Heart Disease (CHD) Atlas represents MRI data sets, physiologic clinical data and computer models from adults and children with various congenital heart defects. The data have been acquired from several clinical centers including Rady...
DETERMINE Defibrillators to Reduce Risk by Magnetic Resonance Imaging Evaluation, is a prospective, multicenter, randomized clinical trials in patients with coronary artery diseases and mild-to-moderate left ventricular dysfunction. The primary objective...
MESA Multi-Ethnic Study of Atherosclerosis, is a large-scale cardiovascular population study (>6,500 participants) conducted in six centres in the USA. It aims to investigate the manifestation of subclinical to clinical cardiovascular disease before...
OASIS The Open Access Series of Imaging Studies (OASIS) is a project aimed at making MRI data sets of the brain freely available to the scientific community. Two datasets are available: a cross-sectional and a longitudinal set.
- Cross-sectional MRI Data in Young, Middle Aged, Nondemented and Demented Older Adults: This set consists of a cross-sectional collection of 416 subjects aged 18 to 96. For each subject, 3 or 4 individual T1-weighted MRI scans obtained in single scan sessions are included. The subjects are all right-handed and include both men and women. 100 of the included subjects over the age of 60 have been clinically diagnosed with very mild to moderate Alzheimer’s disease (AD). Additionally, a reliability data set is included containing 20 nondemented subjects imaged on a subsequent visit within 90 days of their initial session.
- Longitudinal MRI Data in Nondemented and Demented Older Adults: This set consists of a longitudinal collection of 150 subjects aged 60 to 96. Each subject was scanned on two or more visits, separated by at least one year for a total of 373 imaging sessions. For each subject, 3 or 4 individual T1-weighted MRI scans obtained in single scan sessions are included. The subjects are all right-handed and include both men and women. 72 of the subjects were characterized as nondemented throughout the study. 64 of the included subjects were characterized as demented at the time of their initial visits and remained so for subsequent scans, including 51 individuals with mild to moderate Alzheimer’s disease. Another 14 subjects were characterized as nondemented at the time of their initial visit and were subsequently characterized as demented at a later visit.
SCMR Consensus Data The SCMR Consensus Dataset is a set of 15 cardiac MRI studies of mixed pathologies (5 healthy, 6 myocardial infarction, 2 heart failure and 2 hypertrophy), which were acquired from different MR machines (4 GE, 5 Siemens, 6 Philips). The main objectives...
Sunnybrook Cardiac Data The Sunnybrook Cardiac Data (SCD) , also known as the 2009 Cardiac MR Left Ventricle Segmentation Challenge data, consist of 45 cine-MRI images from a mixed of patients and pathologies: healthy , hypertrophy , heart failure with infarction and heart...
Lung Image Database Consortium (LIDC)
Preliminary clinical studies have shown that spiral CT scanning of the lungs can improve early detection of lung cancer in high-risk individuals. Image processing algorithms have the potential to assist in lesion detection on spiral CT studies, and to assess the stability or change in lesion size on serial CT studies. The use of such computer-assisted algorithms could significantly enhance the sensitivity and specificity of spiral CT lung screening, as well as lower costs by reducing physician time needed for interpretation.
The intent of the Lung Imaging Database Consortium (LIDC) initiative was to support a consortium of institutions to develop consensus guidelines for a spiral CT lung image resource and to construct a database of spiral CT lung images. The investigators funded under this initiative created a set of guidelines and metrics for database use and for developing a database as a test-bed and showcase for those methods. The database is available to researchers and users through the Internet and has wide utility as a research, teaching, and training resource.
Specifically, the LIDC initiative aims were to provide:
- a reference database for the relative evaluation of image processing or CAD algorithms and
- a flexible query system that will provide investigators the opportunity to evaluate a wide range of technical parameters and de-identified clinical information within this database that may be important for research applications.
This resource will stimulate further database development for image processing and CAD evaluation for applications that include cancer screening, diagnosis, and image guided intervention, and treatment. Therefore, the NCI encourages investigator-initiated grant applications that utilize the database in their research. NCI also encourages investigator-initiated grant applications that provide tools or methodology that may improve or complement the mission of the LIDC.
Cancer imaging data sets across various cancer types (e.g. carcinoma, lung cancer, myeloma) and various imaging modalities. The image data in The Cancer Imaging Archive (TCIA) is organized into purpose-built collections of subjects. The subjects typically have a cancer type and/or anatomical site (lung, brain, etc.) in common. Each link in the table below contains information concerning the scientific value of a collection, information about how to obtain any supporting non-image data which may be available, and links to view or download the imaging data. To support reproducibility in scientific research, TCIA supports Digital Object Identifiers (DOIs) which allow users to share subsets of TCIA data referenced in a research manuscript.
Belarus tuberculosis portal
Tuberculosis (TB) is a major problem of Belarus Public Health .Recently situation has been complicated with emergence and development of MDR/XDR TB and HIV/TB which require long-term treatment. Many and the most severe cases usually disseminate across the country to different TB dispensaries. The ability of leading Belarus TB specialists to follow such patients will be greatly improved by using a common database containing patients’ radiological images, lab work and clinical data. This will also significantly improve adherence to the treatment protocol and result in a better record of the treatment outcomes. Criteria for inclusion clinical cases in the database of the portal - patients admitted to the MDR-TB department of RSPC of Pulmonology and Tuberculosis with diagnosed or suspected of MDR-TB, which conducted CT – study (± 2 months from the date of registration) Belarus dataset have both chest X-rays and CT scans of the same patient.
DDSM: Digital Database for Screening Mammography
The Digital Database for Screening Mammography (DDSM) is a resource for use by the mammographic image analysis research community. Primary support for this project was a grant from the Breast Cancer Research Program of the U.S. Army Medical Research and Materiel Command. The DDSM project is a collaborative effort involving co-p.i.s at the Massachusetts General Hospital (D. Kopans, R. Moore), the University of South Florida (K. Bowyer), and Sandia National Laboratories (P. Kegelmeyer). Additional cases from Washington University School of Medicine were provided by Peter E. Shile, MD, Assistant Professor of Radiology and Internal Medicine. Additional collaborating institutions include Wake Forest University School of Medicine (Departments of Medical Engineering and Radiology), Sacred Heart Hospital and ISMD, Incorporated. The primary purpose of the database is to facilitate sound research in the development of computer algorithms to aid in screening. Secondary purposes of the database may include the development of algorithms to aid in the diagnosis and the development of teaching or training aids. The database contains approximately 2,500 studies. Each study includes two images of each breast, along with some associated patient information (age at time of study, ACR breast density rating, subtlety rating for abnormalities, ACR keyword description of abnormalities) and image information (scanner, spatial resolution, ...). Images containing suspicious areas have associated pixel-level "ground truth" information about the locations and types of suspicious regions. Also provided are software both for accessing the mammogram and truth images and for calculating performance figures for automated image analysis algorithms.
Prostate cancer (CaP) has been reported on a worldwide scale to be the second most frequently diagnosed cancer of men accounting for 13.6% (Ferlay et al. (2010)). Statistically, in 2008, the number of new diagnosed cases was estimated to be 899, 000 with no less than 258, 100 deaths (Ferlay et al. (2010)).
Magnetic resonance imaging (MRI) provides imaging techniques allowing to diagnose and localize CaP. The I2CVB provides a multi-parametric MRI dataset to help at the development of computer-aided detection and diagnosis (CAD) system. Access: http://i2cvb.github.io/
MRI Lesion Segmentation in Multiple Sclerosis Database
Emergency Tele-Orthopedics X-ray Digital Library
Needle EMG MUAP Time Domain Features
DICOM image sample sets These datasets are exclusively available for research and teaching. You are not authorized to redistribute or sell them, or use them for commercial purposes.
All these DICOM files are compressed in JPEG2000 transfer syntax.
SCR database: Segmentation in Chest Radiographs
The automatic segmentation of anatomical structures in chest radiographs is of great importance for computer-aided diagnosis in these images. The SCR database has been established to facilitate comparative studies on segmentation of the lung fields, the heart and the clavicles in standard posterior-anterior chest radiographs.
In the spirit of cooperative scientific progress, we freely share the SCR database and are committed to maintaining a public repository of results of various algorithms on these segmentation tasks. On thes pages, instructions can be found on downloading the database and uploading results, and benchmark results of various methods can be inspected.
Medical Image Databases & Libraries
e-Anatomy.org - Interactive Atlas of Anatomy - e-anatomy is an anatomy e-learning web site. More than 1500 slices from normal CT and MR exams were selected in order to cover the entire sectional anatomy of human body. Images were labeled using Terminologia Anatomica. A user-friendly interface allows to cine through multi-slice image series combined with interactive textual information, 3D models and anatomy drawings.
Medical Pictures and Definitions - Welcome to the largest database of medical pictures and definitions on the Internet. There are many sites sites that provide medical information but very few that provide medical pictures. As far as we know we are the only one that provides a medical picture database with basic information about each term pictured. Editor's Note: Nice website with free access & no pesky registration to 1200+ health and medical related images with definitions.
Nucleus Medical Art - Medical Illustrations, Medical Art. Includes 3D animations. "Nucleus Medical Art, Inc. is a leading creator and distributor of medical illustrations, medical animations, and interactive multimedia for publishing, legal, healthcare, entertainment, pharmaceutical, medical device, academia and other markets, both in the U.S. and abroad. Editors Note: Great website.
Medical Image Databases on the Internet (UTHSCSA Library) - A directory of links to websites with topic specific medical related images.
Surgery Videos - A National Library of Medicine MedlinePlus collection of links to 100s and 100s of different surgical procedures. You must have RealPlayer media player on your computer to view these videos which are free of charge.
The ADAM Medical Encyclopedia with Illustrations. Perhaps one of the best illustrated medical works on the internet today, the ADAM Medical Encyclopedia includes over 4,000 articles about diseases, tests, symptoms, injuries, and surgeries. It also contains an extensive library of medical photographs and illustrations to back up those 4,000 articles. These illustrations and articles are free to the public.
Hardin MD - Medical and Disease Pictures, is a Free and established resource that has been offered by the University of Iowa for quite some time. The home page is in directory style where users will have to drill down to find the images they are looking for, many of which go offsite. Nevertheless, Hardin MD is an excellent gateway to 1,000s of detailed medical photos and illustrations.
Health Education Assets Library (HEAL) - Health on the Net Foundation Media Gallery Headquartered in Switzerland, (HON) is an international body that seeks to encourage ethical provision of online health information. "HONmedia (the image gallery) is an unique repository of over 6'800 medical images and videos, pertaining to 1,700 topics and themes. This peerless database has been created manually by HON and new image links are constantly being added from the world-wide Web. HON encourages users to make their own image links available via the Submit an image link." Library includes anatomical images, visual affects of diseases and conditions and procedures.
Public Health Image Library (PHIL) Created by a Working Group at the Centers for Disease Control and Prevention (CDC), the PHIL offers an organized, universal electronic gateway to CDC's pictures. We welcome public health professionals, the media, laboratory scientists, educators, students, and the worldwide public to use this material for reference, teaching, presentation, and public health messages. The content is organized into hierarchical categories of people, places, and science, and is presented as single images, image sets, and multimedia files.
Images from the History of Medicine - This system provides access to the nearly 60,000 images in the prints and photograph collection of the History of Medicine Division (HMD) of the U.S. National Library of Medicine (NLM). The collection includes portraits, pictures of institutions, caricatures, genre scenes, and graphic art in a variety of media, illustrating the social and historical aspects of medicine.
Pozemedicale.org - Collection of medical images in Spanish, Italian, Portuguese and Italian.
Old Medical Pictures: Hundreds of fascinating and interesting old, but high quality photographs and images from the late 19th and early 20th century.
Subject Speciality Image Libraries and Collections
Anatomy of the Human Body by Henry Gray - The Bartleby.com edition of Gray’s Anatomy of the Human Body features 1,247 vibrant engravings—many in color—from the classic 1918 publication.
The Crookston Collection - A collection of medical slides taken by Dr. John H. Crookston that have been digitized and are available to the public and doctors.
DAVE Project - A searchable library of gastrointestinal endoscopic video clips covering a wide spectrum endoscopic imaging.
Dermnet - Browsable collection of over 8,000 high quality, dermatology images.
Interactive Dermatology Atlas - Image reference source for common and uncommon skin problems.
The Multi-Dimensional Human Embryo is a collaboration funded by the National Institute of Child Health and Human Development (NICHD) to produce and make available over the internet a three-dimensional image reference of the Human Embryo based on magnetic resonance imaging.
GastroLab Endoscopy Archives Was initiated in 1996 with the goal of maintaining an endoscopic image gallery free to use for all interested health care personals.
MedPix Is a Radiology and Medical Picture Databases resource tool. The home page interface is confusing and the entire website design is not user-friendly and has a mid 1990s feel to it. However, if you have the time (patience) it could prove to be an important resource for some.
OBGYN.net Image Library - This site is devoted entirely to providing access to images of interest to women's health. In addition to providing you with access to OBGYN.net images we also point to other women's health related images on the Internet. Because of the graphic nature of the material some individuals may prefer not to view these images.They are provided for educational purposes only.
VIA Group Public Databases
Documented image databases are essential for the development of quantitative image analysis tools especially for tasks of computer-aided diagnosis (CAD). In collaboration with the I-ELCAP group we have established two public image databases that contain lung CT images in the DICOM format together with documentation of abnormalities by radiologists. Please access the links below for more details:
CVonline: Image Databases Access: http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm
The USC-SIPI Image Database The USC-SIPI image database is a collection of digitized images. It is maintained primarily to support research in image processing, image analysis, and machine vision. The first edition of the USC-SIPI image database was distributed in 1977 and many new images have been added since then.
The database is divided into volumes based on the basic character of the pictures. Images in each volume are of various sizes such as 256x256 pixels, 512x512 pixels, or 1024x1024 pixels. All images are 8 bits/pixel for black and white images, 24 bits/pixel for color images. The following volumes are currently available:
Textures Brodatz textures, texture mosaics, etc. Aerials High altitude aerial images Miscellaneous Lena, the mandrill, and other favorites Sequences Moving head, fly-overs, moving vehicles
2. Challenges/Contest Data
Visual Concept Extraction Challenge in Radiology Manually annotated radiological data of several anatomical structures (e.g. kidney, lung, bladder, etc.) from several different imaging modalities (e.g. CT and MR). They also provide a cloud computing instance that anyone can use to develop and evaluate models against benchmarks.
Grand Challenges in Biomedical Image Analysis
A collection of biomedical imaging challenges in order to facilitate better comparisons between new and existing solutions, by standardizing evaluation criteria. You can create your own challenge as well. As of this writing, there are 92 challenges that provide downloadable data sets.
DREAM Challenges pose fundamental questions about systems biology and translational medicine. Designed and run by a community of researchers from a variety of organizations, our challenges invite participants to propose solutions — fostering collaboration and building communities in the process. Expertise and institutional support are provided by Sage Bionetworks, along with the infrastructure to host challenges via their Synapse platform. Together, we share a vision allowing individuals and groups to collaborate openly so that the “wisdom of the crowd” provides the greatest impact on science and human health.
- The Digital Mammography DREAM Challenge.
- ICGC-TCGA DREAM Somatic Mutation Calling RNA Challenge (SMC-RNA)
- DREAM Idea Challenge
- These were the active challenges at the time of adding, many more past challenges and upcoming challenges are present!
Kaggle diabetic retinopathy
High-resolution retinal images that are annotated on a 0–4 severity scale by clinicians, for the detection of diabetic retinopathy. This data set is part of a completed Kaggle competition, which is generally a great source for publicly available data sets.
Cervical Cancer Screening
In this kaggle competition, you will develop algorithms to correctly classify cervix types based on cervical images. These different types of cervix in our data set are all considered normal (not cancerous), but since the transformation zones aren't always visible, some of the patients require further testing while some don't.
Multiple sclerosis lesion segmentation
challenge 2008. A collection of brain MRI scans to detect MS lesions.
Multimodal Brain Tumor Segmentation Challenge
Large data set of brain tumor magnetic resonance scans. They’ve been extending this data set and challenge each year since 2012.
A new initiative by the Foundation for the National Institutes of Health and Sage Bionetworks to host a series of challenges to improve cancer screening. The first is for digital mammography readings. The second is for lung cancer detection. The challenges are not yet launched.
EEG Challenge Datasets on Kaggle
- Melbourne University AES/MathWorks/NIH Seizure Prediction - Predict seizures in long-term human intracranial EEG recordings
- American Epilepsy Society Seizure Prediction Challenge - Predict seizures in intracranial EEG recordings
- UPenn and Mayo Clinic's Seizure Detection Challenge - Detect seizures in intracranial EEG recordings
- Grasp-and-Lift EEG Detection - Identify hand motions from EEG recordings
Challenges track in MICCAI Conference
The Medical Image Computing and Computer Assisted Intervention. Most of the challenges would've been covered by websites like grand-challenges etc. You can still see all of them under the "Satellite Events" tab of the conference sites.
- 2017 - http://www.miccai2017.org/satellite-events
- 2016 - http://www.miccai2016.org/en/SATELLITE-EVENTS.html
- 2015 - https://www.miccai2015.org/frontend/index.php?page_id=589
International Symposium on Biomedical Imaging (ISBI)
The IEEE International Symposium on Biomedical Imaging (ISBI) is a scientific conference dedicated to mathematical, algorithmic, and computational aspects of biomedical imaging, across all scales of observation. Most of these challenges will be listed in grand-challenges. You can still access it by visiting the "Challenges" tab under "Program" in each year's website.
- 2017 - http://biomedicalimaging.org/2017/challenges/
- 2016 - http://biomedicalimaging.org/2016/?page_id=416
3. Data derived from Electronic Health Records (EHRs)
Building the graph of medicine from millions of clinical narratives
Co-occurence statistics for medical terms extracted from 14 million clinical notes and 260,000 patients.
Learning Low-Dimensional Representations of Medical Concept
Low-dimensional embeddings of medical concepts constructed using claims data. Note that this paper utilizes data from Building the graph of medicine from millions of clinical narratives
MIMIC-III, a freely accessible critical care database
Anonymized critical care EHR database on 38,597 patients and 53,423 ICU admissions. Requires registration.
4. National Healthcare Data
Centers for Disease Control and Prevention (CDC)
Data from the CDC on many areas, including:
- Child Vaccinations
- Flu Vaccinations
- Health Statistics
- Injury & Violence
- Motor Vehicle
- Pregnancy & Vaccination
- Smoking & Tobacco Use
- Teen Vaccinations
- Traumatic Brain Injury
- Web Metrics
Data from the Centers for Medicare & Medicaid Services (CMS) on hospitals, nursing homes, physicians, home healthcare, dialysis, and device providers.
Landing page: https://data.medicare.gov
Texas Public Use Inpatient Data File Data on 11 Million inpatient visits with diagnosis, procedure codes and outcomes from Texas between 2006 & 2009.
Dollars for Doctors
Propublica investigation of money paid by pharmaceutical companies to doctors.
Search tool: https://projects.propublica.org/docdollars/
Data request: https://projects.propublica.org/data-store/sets/health-d4d-national-2
Physician interaction network obtained through a freedom of information act request. Covers nearly 1 million entities.
Main page: http://www.docgraph.com
5. UCI Datasets
Liver Disorders Data Set
Data on 345 patients with and without liver disease. Features are 5 blood biomarkers thought to be involved with liver disease.
Thyroid Disease Data Set
Breast Cancer Data Set
Heart Disease Data Set
Lymphography Data Set
6. Biomedical Literature
PMC Open Access Subset
Collection of all the full-text, open access articles in Pubmed central.
Archived files: http://www.ncbi.nlm.nih.gov/pmc/tools/ftp/#Data_Mining
6. TREC Precision Medicine / Clinical Decision Support Track
Text REtrieval Conference (TREC) is running a track on Precision Medicine / Clinical Decision Support from 2014.
2014 Clinical Decision Support Track
Focus: Retrieval of biomedical articles relevant for answering generic clinical questions about medical records.
Information and Data: http://www.trec-cds.org/2014.html
2015 Clinical Decision Support Track
Focus: Retrieval of biomedical articles relevant for answering generic clinical questions about medical records.
Information and Data: http://www.trec-cds.org/2015.html
2016 Clinical Decision Support Track
Focus: Retrieval of biomedical articles relevant for answering generic clinical questions about medical records. Actual electronic health record (EHR) patient records are be used instead of synthetic cases.
Information and Data: http://www.trec-cds.org/2016.html
2017 Clinical Decision Support Track
Focus: Retrieve useful precision medicine-related information to clinicians treating cancer patients.
Information and Data: http://www.trec-cds.org/2017.html