Data

MedShapeNet contains over 100,000 medical shapes, including bones, organs, vessels, muscles, etc., as well as surgical instruments.

Related publication: MedShapeNet – A Large-scale Dataset of 3D Medical Shapes for Computer Vision

OpenPath, a large dataset of 208,414 pathology images paired with natural language descriptions.

Related publication:  Huang, Z., Bianchi, F., Yuksekgonul, M. et al. A visual–language foundation model for pathology image analysis using medical Twitter. Nat Med (2023). https://doi.org/10.1038/s41591-023-02504-3

This is a dataset used in the Med-HALT research paper. Med-HALT provides a diverse multinational dataset derived from medical examinations across various countries and includes multiple innovative testing modalities. Med-HALT includes two categories of tests reasoning and memory-based hallucination tests, designed to assess LLMs’ problem-solving and information retrieval abilities. This research paper focuses on the challenges posed by hallucinations in large language models (LLMs), particularly in the context of the medical domain. The authors propose a new benchmark and dataset, Med-HALT (Medical Domain Hallucination Test), designed specifically to evaluate hallucinations.

The MIMIC-IV-ECHO module contains more than 500,000 echocardiograms across 7,243 studies from 4,579 distinct patients.  This subset contains echocardiograms for patients who appear in the MIMIC-IV Clinical Database and were admitted between 2017 and 2019.

Related publication: Gow, B., Pollard, T., Greenbaum, N., Moody, B., Johnson, A., Herbst, E., Waks, J. W., Eslami, P., Chaudhari, A., Carbonati, T., Berkowitz, S., Mark, R., & Horng, S. (2023). MIMIC-IV-ECHO: Echocardiogram Matched Subset (version 0.1). PhysioNet. https://doi.org/10.13026/ef48-v217.

The CodiEsp corpus contains manually coded clinical cases. All documents are in Spanish language and CIE10 is the coding terminology (it is the Spanish version of ICD10-CM and ICD10-PCS). The CodiEsp corpus has been randomly sampled into three subsets: the train, the development, and the test set. The train set contains 500 clinical cases, and the development and test set 250 clinical cases each.

Related publication: Iker de la Iglesia, María Vivó, Paula Chocrón, Gabriel de Maeztu, Koldo Gojenola, Aitziber Atutxa, An open source corpus and automatic tool for section identification in Spanish health records,Journal of Biomedical Informatics,2023,104461,ISSN 1532-0464,https://doi.org/10.1016/j.jbi.2023.104461.

3 key datasets for endocscopy:

1. Kvasir-dataset-v2 contains 8,000 images, 8 classes, 1,000 images for upper and lower endoscopy in each class:

2.The Kvasir-Capsule dataset

3.Kvasir SEG dataset

Related publications:Konstantin Pogorelov, Kristin Ranheim Randel,Halvorsen, Kvasir, et al.: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection, In MMSys’17 Proceedings of the 8th ACM on Multimedia Systems Conference (MMSYS), Pages 164-169 Taipei, Taiwan, June 20-23, 2017.

It contains 21 videos with a total number of 5, 525 frames annotated and verified by medical doctors (experienced endoscopists). The videos are divided into four classes of predefined bowel-preparation qualities.

Related publication: Konstantin Pogorelov, Kristin Ranheim Randel, Thomas de Lange, Sigrun Losada Eskeland, Carsten Griwodz, Dag Johansen, Concetto Spampinato, Mario Taschwer, Mathias Lux, Peter Thelin Schmidt, Michael Riegler, PÃ¥l Halvorsen, Nerthus: A Bowel Preparation Quality Video Dataset, In MMSys’17 Proceedings of the 8th ACM on Multimedia Systems Conference, Pages 170-174, Taipei, Taiwan, June 20-23, 2017.

This dataset comprises 18,481 images extracted from 523 small bowel capsule endoscopy videos. It has annotated 12,3320 images with 23,033 disease lesions and combined with 6,161 normal mucosa images. The annotations are provided in YOLO format.

Related publication: Yokote A, Umeno J, Kawasaki K, Fujioka S, Fuyuno Y, Matsuno Y, Yoshida Y, Imazu N, Miyazono S, Moriyama T, Kitazono T, Torisu T. Small bowel capsule endoscopy examination and open access database with artificial intelligence: The SEE-artificial intelligence project. DEN Open. 2023 Jun 22;4(1):e258. doi: 10.1002/deo2.258. PMID: 37359150; PMCID: PMC10288072.

More than 80 open source healthcare datasets available through the AWS Open Data Sponsorship Program.

3 datasets:

• NHS UK Q/A, 24,665 question and answer pairs, Prompt used: f53cf99826, Generated via OpenGPT using data available on the NHS UK Website. Download here.(Click on view Raw data)
• NHS UK Conversations, 2,354 unique conversations, Prompt used: f4df95ec69, Generated via OpenGPT using data available on the NHS UK Website. Download here. (Click on view Raw data)
• Medical Task/Solution, 4,688 pairs generated via OpenGPT using GPT-4, prompt used: 5755564c19. Download here. (Click on view Raw data)

Data to predict the course of Parkinson’s disease (PD) using protein abundance data. The core of the dataset consists of protein abundance values derived from mass spectrometry readings of cerebrospinal fluid (CSF) samples gathered from several hundred patients. Each patient contributed several samples over the course of multiple years while they also took assessments of PD severity.This is a time-series code dataset with Kaggle’s time-series API.

The data series include three datasets, collected under distinct circumstances:

• The tDCS FOG (tdcsfog) dataset, comprising data series collected in the lab, as subjects completed a FOG-provoking protocol.
• The DeFOG (defog) dataset, comprising data series collected in the subject’s home, as subjects completed a FOG-provoking protocol
• The Daily Living (daily) dataset, comprising one week of continuous 24/7 recordings from sixty-five subjects. Forty-five subjects exhibit FOG symptoms and also have series in the defog dataset, while the other twenty subjects do not exhibit FOG symptoms and do not have series elsewhere in the data.

The National Institutes of Health’s All of Us Research Program is building one of the largest biomedical data resources of its kind.

600,000+ participants

350,000+ EHR records

450,000+ biomedical specimen data

3 metastatic cancer  datasets available through AWS API.

• Time Series Dataset – Each row in the time series dataset represents a point in time, in units of days indexed from each patient’s initial gamma knife radiosurgery. Dataset variables include clinical details related to medication changes, imaging timing/references to raw imaging files, procedure timing, clinical follow up, and outcomes.
• Individual Dataset – Each row represents an individual patient with demographic details and summary clinical data.
• Gamma Knife Details Dataset – Each row represents an individual gamma knife target to provide further details about available gamma knife radiosurgery.

The Dermofit Image Library is a collection of 1,300 focal high quality skin lesion images collected under standardised conditions with internal colour standards. The lesions span across ten different classes including melanomas, seborrhoeic keratosis and basal cell carcinomas. Each image has a gold standard diagnosis based on expert opinion (including dermatologists and dermatopathologists). Images consist of a snapshot of the lesion surrounded by some normal skin.The Dermofit Image Library is available under an academic licence. There is a one-off £75 licence fee associated with this product.

Related publication: Rees, Aldridge, Fisher, Ballerini (2013), A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions, Color Medical Image Analysis, Lecture Notes in Computational Vision and Biomechanics 6 (M. E. Celebi, G. Schaefer (eds.))

A dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data,  18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases.

The EchoNet-Peds database includes 7,643 labeled echocardiogram videos and human expert annotations (measurements, tracings, and calculations) to provide a baseline to study cardiac motion and chamber sizes. The database includes patients ranging from 0-18 years (43% female) with a wide range of sizes.

Related publication: Reddy CD, Lopez L, Ouyang D, Zou JY, He B. Video-Based Deep Learning for Automated Assessment of Left Ventricular Ejection Fraction in Pediatric Patients. J Am Soc Echocardiogr. 2023 Feb 6:S0894-7317(23)00068-8. doi: 10.1016/j.echo.2023.01.015. Epub ahead of print. PMID: 36754100.

All BraTS multimodal scans are available as NIfTI files (.nii.gz) which were were acquired with different clinical protocols and various scanners from multiple (n=19) institutions.The overall survival (OS) data, defined in days, are included in a comma-separated value (.csv) file with correspondences to the pseudo-identifiers of the imaging data. The .csv file also includes the age of patients, as well as the resection status.

Related publication:B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al. “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)”, IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI: 10.1109/TMI.2014.2377694

The data in this challenge contains whole-slide images (WSI) of hematoxylin and eosin (H&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. 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.The test data set contains 500 slides. 1000 slides with 5 slides per patient .

The dataset contains 7,471 chest X-ray images in .png  file format and 3955 patients radiology text reports available in .XML format. Each image has been paired with four captions such as Impressions, Findings, Comparison and Indication that provide clear descriptions of the salient entities and events.

Original data source : https://openi.nlm.nih.gov/

Open-i provides access to over 3.7 million images from about 1.2 million PubMed Central^® articles; 7,470 chest x-rays with 3,955 radiology reports; 67,517 images from NLM History of Medicine collection; and 2,064 orthopedic illustrations.

A  synthetic dataset of brain images simulated across 42 different MR protocols and based on 500 different reference brains from the Human Connectome Project (HCP) (Van Essen et al., 2012), leading to 21,000 simulated brain images,

Related Publication: You S, Reyes M. Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation. Frontiers in Neuroimaging. 2022;1.

An open-source data collection consisting a total of 955 T1-weighted MRIs (Magnetic Resonance Imaging) with manually segmented diverse lesions and metadata

Related publication: Liew, Sook-Lei. The Anatomical Tracings of Lesions after Stroke (ATLAS) Dataset – Release 2.0, 2021. Inter-university Consortium for Political and Social Research [distributor], 2022-08-08. https://doi.org/10.3886/ICPSR36684.v5

The dataset is a single-institutional, retrospective collection of 922 biopsy-confirmed invasive breast cancer patients, over a decade, having the following data components:

1. Demographic, clinical, pathology, treatment, outcomes, and genomic data: Collected from a variety of sources including clinical notes, radiology report, and pathology reports.
2. Pre-operative dynamic contrast enhanced (DCE)-MRI: Downloaded from PACS systems and de-identified for The Cancer Imaging Archive (TCIA) release in DICOM format.
3. Locations of lesions in DCE-MRI: Annotations on the DCE-MRI images by radiologists.
4. Imaging features from DCE-MRI: A set of 529 computer-extracted imaging features by inhouse software.

Related publication: Saha, A., Harowicz, M.R., Grimm, L.J., Kim, C.E., Ghate, S.V., Walsh, R. and Mazurowski, M.A., 2018. A machine learning approach to radiogenomics of breast cancer.

The National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The NHANES interview includes demographic, socioeconomic, dietary, and health-related questions. The survey examines a nationally representative sample of about 5,000 persons each year. Findings from this survey will be used to determine the prevalence of major diseases and risk factors for diseases.

This is an original dataset of stringency of public health policy measures that were adopted in response to COVID-19 worldwide by governments at national and sub-national levels. The data set covers governments’ policy responses between January 24, 2020 and December 31, 2020.

Related publication: Shvetsova, O., Zhirnov, A., Adeel, A.B. et al. Protective Policy Index (PPI) global dataset of origins and stringency of COVID 19 mitigation policies. Sci Data 9, 319 (2022). https://doi.org/10.1038/s41597-022-01437-9

Multiple datasets available:

1. silent-cchs-ecg: Diagnosing ‘silent’ heart attack (48,000 ECG waveforms)
2. brca-psj-path: Identifying high-risk breast cancer (175,000 biopsy slides)
3. arrest-ntuh-ecg: Subtyping cardiac arrest (24,106 ECG waveforms)
4. fracture-aimi-xray: Predicting fractures (64,000 chest x-rays)
5. covid-psj-xray: Emergency triage of Covid-19 patients (7,500 chest x-rays)

A dataset consisting of 53,449 audio samples (over 552 hours in total) crowd-sourced from 36,116 participants through our COVID-19 Sounds app. It also provides participants’ self-reported COVID-19 testing status with 2,106 samples tested positive.

Related publication: COVID-19 Sounds: A Large-Scale Audio Dataset for Digital Respiratory Screening

Related publication: Jain, S., Agrawal, A., Saporta, A., Truong, S. Q., Nguyen Duong, D., Bui, T., Chambon, P., Lungren, M., Ng, A., Langlotz, C., & Rajpurkar, P. (2021). RadGraph: Extracting Clinical Entities and Relations from Radiology Reports (version 1.0.0). PhysioNet. https://doi.org/10.13026/hm87-5p47.

200+ datasets of various types with links and papers.Includes search options for datatypes, language and more.

The PH² database includes the manual segmentation, the clinical diagnosis, and the identification of several dermoscopic structures, performed by expert dermatologists, in a set of 200 dermoscopic images.

Related publication: Mendonca T, Ferreira PM, Marques JS, Marcal AR, Rozeira J. PH² – a dermoscopic image database for research and benchmarking. Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:5437-40. doi: 10.1109/EMBC.2013.6610779. PMID: 24110966

Vision-based Fallen Person (VFP290K) dataset consists of 294,713 frames of fallen persons extracted from 178 videos, including 131 scenes in 49 locations. It demonstrated the effectiveness of the features through extensive experiments analyzing the performance shift based on object detection models.

Related publication: VFP290K: A Large-Scale Benchmark Dataset for Vision-based Fallen Person Detection

HiRID is a freely accessible critical care dataset containing data relating to almost 34 thousand adult patient admissions to the Department of Intensive Care Medicine of the Bern University Hospital, Switzerland (ICU), an interdisciplinary 60-bed unit admitting >6,500 patients per year. The dataset contains de-identified demographic information and a total of 681 routinely collected physiological variables, diagnostic test results and treatment parameters from almost 34 thousand admissions during the period from January 2008 to June 2016. Data is stored with a uniquely high time resolution of one entry every two minutes.

Related publication: Faltys, M., Zimmermann, M., Lyu, X., Hüser, M., Hyland, S., Rätsch, G., & Merz, T. (2021). HiRID, a high time-resolution ICU dataset (version 1.1.1). PhysioNet. https://doi.org/10.13026/nkwc-js72.

eICU Collaborative Research Database, a multi-center intensive care unit (ICU)database with high granularity data for over 200,000 admissions to ICUs monitored by eICU Programs across the United States.

Related publication: The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG and Badawi O. Scientific Data (2018). DOI: http://dx.doi.org/10.1038/sdata.2018.178.

The Medical Information Mart for Intensive Care (MIMIC)-IV database provided critical care data for over 40,000 patients admitted to intensive care units at the Beth Israel Deaconess Medical Center (BIDMC).

Related publication: Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L. A., & Mark, R. (2020). MIMIC-IV (version 0.4). PhysioNet. https://doi.org/10.13026/a3wn-hq05.

A dataset of paired Electroencephalography (EEG) and video-infrared eye tracking (ET) recordings from 356 subjects for more than 47 hours in total. A benchmark consisting of 3 evaluation tasks with increasing difficulty is introduced alongside the dataset.

Related publication: EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction

Q-Pain, a dataset for assessing bias in medical QA in the context of pain management. 55 medical question-answer pairs across five different types of pain management: each question includes a detailed patient-specific medical scenario (“vignette”) designed to enable the substitution of multiple different racial and gender “profiles” and to evaluate whether bias is present when answering whether or not to prescribe medication.

Related publication: Logé, C., Ross, E., Dadey, D. Y. A., Jain, S., Saporta, A., Ng, A., & Rajpurkar, P. (2021). Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management (version 1.0.0). PhysioNet. https://doi.org/10.13026/2tdv-hj07.

Dataset contributes significantly to the research community by providing 1) 1,256 combinations of relation annotations between 29 CXR anatomical locations (objects with bounding box coordinates) and their attributes, structured as a scene graph per image, 2) over 670,000 localized comparison relations (for improved, worsened, or no change) between the anatomical locations across sequential exams, as well as 3) a manually annotated gold standard scene graph dataset from 500 unique patients.

Related publication: Wu, J., Agu, N., Lourentzou, I., Sharma, A., Paguio, J., Yao, J. S., Dee, E. C., Mitchell, W., Kashyap, S., Giovannini, A., Celi, L. A., Syeda-Mahmood, T., & Moradi, M. (2021). Chest ImaGenome Dataset (version 1.0.0). PhysioNet. https://doi.org/10.13026/wv01-y230.

TDC includes 66 AI-ready datasets spread across 22 learning tasks and spanning the discovery and development of safe and effective medicines. TDC also provides an ecosystem of tools and community resources, including 33 data functions and diverse types of data splits, 23 strategies for systematic model evaluation, 17 molecule generation oracles, and 29 public leaderboards.

Related publication: Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development

The RAD-ChestCT dataset is a imaging dataset developed by Duke MD/PhD student Rachel Draelos during her Computer Science PhD supervised by Lawrence Carin. The full dataset includes 35,747 chest CT scans from 19,661 adult patients. This Zenodo repository contains an initial release of 3,630 chest CT scans, approximately 10% of the dataset.

Related publication: Draelos et al., “Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes,” Medical Image Analysis 2021. DOI: 10.1016/j.media.2020.101857

A dataset consists of 70 melanoma and 100 naevus images from the digital image archive of the Department of Dermatology of the University Medical Center Groningen (UMCG) used for the development and testing of the MED-NODE system for skin cancer detection from macroscopic images. The file contains 170 images (70 melanoma and 100 nevi cases).

Related publications: I. Giotis, N. Molders, S. Land, M. Biehl, M.F. Jonkman and N. Petkov: “MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images”, Expert Systems with Applications, 42 (2015), 6578-6585

BIGBIO a community library of 126+ biomedical NLP datasets currently covering 12 task categories and 10+ languages with • programmatic access. BIGBIO enables reproducible data-centric machine learning workflows, by focusing on programmatic access to datasets and their metadata in a uniform format.

Related Publication: BIGBIO: A Framework for Data-Centric Biomedical Natural Language Processing

A community-driven platform for reporting biomedical AI systems.

Related publication:  Matschinske, J., Alcaraz, N., Benis, A. et al. The AIMe registry for artificial intelligence in biomedical research. Nat Methods (2021), DOI: 10.1038/s41592-021-01241-0, https://rdcu.be/cv5H7

The dataset consists of 2,298 samples of six different types of skin lesions. Each sample consists of a clinical image and up to 22 clinical features including the patient’s age, skin lesion location, Fitzpatrick skin type, and skin lesion diameter. ll BCC, SCC, and MEL are biopsy-proven.In total, there are 1,373 patients, 1,641 skin lesions, and 2,298 images present in the dataset. The remaining ones may have clinical diagnosis according to a consensus of a group of dermatologists. In total, approximately 58% of the samples in this dataset are biopsy-proven.

Related publication: PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones

The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data.

Related publication: ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

2201 images with diagnoses based on biopsy or clinical impression.174 disease classes for the model training.

Biomedical relation extraction dataset (BioRED) with multiple entity types (e.g. gene/protein, disease, chemical) and relation pairs (e.g. gene–disease; chemical–chemical) at the document level, on a set of 600 PubMed abstracts.

Related dataset: Ling Luo, et al. BioRED: a rich biomedical relation extraction dataset, Briefings in Bioinformatics, 2022

The RadImageNet database includes 1.35 million annotated CT, MRI, and ultrasound images of musculoskeletal, neurologic, oncologic, gastrointestinal, endocrine, and pulmonary pathology. The RadImageNet database contains medical images of 3 modalities, 11 anatomies, and 165 pathologic labels.

BRAX dataset provides 40,967 images, 24,959 imaging studies for 19,351 patients presenting to the Hospital Israelita Albert Einstein. All images have been verified by trained radiologists and de-identified to protect patient privacy. Fourteen labels were derived from free-text radiology reports written in Brazilian Portuguese using Natural Language Processing.

Related publication: BRAX, a Brazilian labeled chest X-ray dataset

The MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging.Project MONAI also includes MONAI Label, an intelligent open source image labeling and learning tool that helps researchers and clinicians collaborate, create annotated datasets, and build AI models in a standardized MONAI paradigm.

This collection comprises multi-parametric magnetic resonance imaging (mpMRI) scans for de novo Glioblastoma (GBM) patients from the University of Pennsylvania Health System, coupled with patient demographics, clinical outcome (e.g., overall survival, genomic information, tumor progression), as well as computer-aided and manually-corrected segmentation labels of multiple histologically distinct tumor sub-regions, computer-aided and manually-corrected segmentations of the whole brain, a rich panel of radiomic features along with their corresponding co-registered mpMRI volumes in NIfTI format.

630 patients, 3301 studies, 820,000 + images.

A platform for end-to-end development of machine learning solutions in biomedical imaging.Grand Challenge was developed in 2010 to make it easy for organizers of challenges to set up a website for a particular challenge and to bring all information on challenges in the domain of biomedical image analysis available at one place.This system has been operational since 2017 and has been used by over 300 challenges,70,000 users with more than 1000 algorithms.

A database for evaluating computerized image-based prediction of the 7-point skin lesion malignancy checklist. The dataset includes over 2000 clinical and dermoscopy color images, along with corresponding structured metadata tailored for training and evaluating computer aided diagnosis (CAD) systems.

Related publication: J. Kawahara, S. Daneshvar, G. Argenziano, and G. Hamarneh, “Seven-Point Checklist and Skin Lesion Classification using Multitask Multimodal Neural Nets,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 2, pp. 538–546, 2019.

A free and open platform for validating and sharing BIDS-compliant MRI, PET, MEG, EEG, and iEEG data.720 public datasets and growing.

Webpage: https://openneuro.org/

The EchoNet-LVH dataset includes 12,000 labeled echocardiogram videos and human expert annotations (measurements, tracings, and calculations) to provide a baseline to study cardiac chamber size and wall thickness.

Related publication: High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning

The database includes 154 conventional chest radiographs with a lung nodule (100 malignant and 54 benign nodules) and 93 radiographs without a nodule  The database also includes additional information such as; patient age, gender, diagnosis (malignant or benign), X and Y coordinates of nodule, simple diagram of nodule location. Lung nodule images were classified into five groups according to the degrees of subtlety.

Related publication:  Shiraishi J, Katsuragawa S, lkezoe J, et al: Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. AJR 174:71-74, 2000.

Data was collected from nine healthy volunteers during a study of propofol-induced unconsciousness.  For all subjects, approximately 3 hours of data were recorded while using target-controlled infusion protocol.Data includes continuous electrocardiogram (ECG); interventions included in the study for patient safety, such as administering phenylephrine (a vasopressor);heart rate variability (HRV) and electrodermal activity (EDA).

Related publication: Subramanian, S., Purdon, P., Barbieri, R., & Brown, E. (2021). Behavioral and autonomic dynamics during propofol-induced unconsciousness (version 1.0). PhysioNet. https://doi.org/10.13026/2rbc-1r03.

94 open access ophthalmological imaging datasets containing 507 724 images and 125 videos from 122 364 patients.

The PTB-XL ECG dataset is a large dataset of 21837 clinical 12-lead ECGs from 18885 patients of 10 second length. The raw waveform data was annotated by up to two cardiologists, who assigned potentially multiple ECG statements to each record. Total 71 different ECG statements conform to the SCP-ECG standard and cover diagnostic, form, and rhythm statements.

Related publication: Wagner, P., Strodthoff, N., Bousseljot, R.-D., Kreiseler, D., Lunze, F.I., Samek, W., Schaeffter, T. (2020), PTB-XL: A Large Publicly Available ECG Dataset. Scientific Data. https://doi.org/10.1038/s41597-020-0495-6

A collection of de-identified annotated medical imaging data to foster transparent and reproducible collaborative research. X-rays, CT scans, MRIs,Echocardiography and Dermatology images.

Diverse Dermatology Images (DDI) dataset—the first publicly available, deeply curated, and pathologically confirmed image dataset with diverse skin tones. The DDI was retrospectively selected from reviewing pathology reports in Stanford Clinics from 2010-2020. It has a total of 656 images representing 570 unique patients.

Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks.Currently over 2658 datasets, and more than 34 metrics available.At least 13 datasets with “medical” term search.Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model.

The DCSM dataset consists of 255 randomly selected and fully anonymized overnight lab-based PSG recordings from patients visiting the DCSM for the diagnosis of non-specific sleep related disorders. The DCSM dataset represents a diverse cohort of Danish patients with respect to demographic characteristics, diagnostic background and sleep/non-sleep related medication usage, collected between 2015-2018.

Two publicly-available datasets, DOD-H including 25 healthy volunteers and DOD-O including 55 patients suffering from obstructive sleep apnea (OSA). Both datasets have been scored by 5 sleep technologists from different sleep centers. We developed a framework to compare automated approaches to a consensus of multiple human scorers.

Related publication: A. Guillot, F. Sauvet, E. H. During and V. Thorey, “Dreem Open Datasets: Multi-Scored Sleep Datasets to Compare Human and Automated Sleep Staging,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 9, pp. 1955-1965, Sept. 2020, doi: 10.1109/TNSRE.2020.3011181.

375 heterogeneous cardiac magnetic resonance (CMR) datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany).

Related publication: V. M. Campello et al., “Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge,” in IEEE Transactions on Medical Imaging, vol. 40, no. 12, pp. 3543-3554, Dec. 2021, doi: 10.1109/TMI.2021.3090082.

TCIA data are organized as “collections”; typically these are patient cohorts related by a common disease (e.g. lung cancer), image modality or type (MRI, CT, digital histopathology, etc) or research focus. Supporting data related to the images such as patient outcomes, treatment details, genomics and image analyses are also provided when available. Over  100+ datasets, many of which are public.

Unstructured notes from the Research Patient Data Registry at Partners Healthcare,Boston,USA (originally developed during the i2b2 project). Clinical Natural Language Processing (NLP) data sets were originally created at a former NIH-funded National Center for Biomedical Computing (NCBC) known as i2b2: Informatics for Integrating Biology and the Bedside. Beginning in 2018, they are officially known as n2c2 (National NLP Clinical Challenges).

A publicly available EMR Question Answering (QA) corpus by creating a large-scale dataset, emrQA, using a novel semi-automated generation framework that allows for minimal expert involvement and re-purposes existing annotations available for other clinical NLP tasks.EmrQA has 1 million question-logical form and 400,000+ question answer evidence pairs. The dataset uses existing NLP task annotations from the i2b2 Challenge datasets.

Related publication: Pampari, A., Raghavan, P., Liang, J.J., & Peng, J. (2018). emrQA: A Large Corpus for Question Answering on Electronic Medical Records. EMNLP.

VSCapture, an open source tool developed in C# programming language on the .NET/Mono platform that allows the tool to run on Windows, Macintosh OS X, Linux Ubuntu operating systems.

Related Publication: Data acquisition from S/5 GE Datex anesthesia monitor using VSCapture.

Related Dataset: The University of Queensland Vital Signs Dataset.

The University of Queensland Vital Signs Dataset contains a wide range of patient monitoring data and vital signs that were recorded during 32 surgical cases where patients underwent anaesthesia at the Royal Adelaide Hospital.

12,625 whole-slide images (WSIs) of prostate biopsies were available for model development (the development set), 393 for performance evaluation during the competition phase (the tuning set), 545 as the internal validation set in the postcompetition phase and 1,071 for external validation from 6 different sites.

Related publication: Bulten, W., Kartasalo, K., Chen, PH.C. et al. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. Nat Med (2022). https://doi.org/10.1038/s41591-021-01620-2

This is a dataset from tertiary care medical college and hospital in India’s cardiology unit which had 14,845 admissions corresponding to 12,238 patients.

Related publication: Bollepalli, S.C.; Sahani, A.K.; Armoundas, A.A. ,et al. An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit. Diagnostics 2022, 12, 241.

https://doi.org/10.3390/diagnostics12020241

The dataset included over 69,000 dermatology images.International Skin Imaging Collaboration (ISIC) is a global partnership that has organized the world’s largest repository of publicly available dermoscopic images, hosted the first public benchmarks for melanoma detection in dermoscopic images, titled “Skin Lesion Analysis Towards Melanoma Detection”, at the IEEE International Symposium of Biomedical Imaging (ISBI).

A dataset of 491 Head CT scans with 193,317 slices, anonymized dicoms for all the scans and the corresponding radiologists’ reads done by three radiologists with an experience of 8, 12 and 20 years in cranial CT interpretation respectively.

Related publication: Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT scan.

Publicly available  suite of tailored deep neural network models for tackling different challenges ranging from screening to risk stratification to treatment planning for patients with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). 

• Chest x-rays: 16,352 CXR images across 14,979 patients Click here
• Chest CT: 201,103 CT slices from 4,501 patients Click here
• Chest point-of-care ultrasound: 29,651 POCUS images Click here
• COVID-Net ICU:1925 records from 385 patients   Click here

Also,expanded to open source TB-Net initiative for tuberculosis screening, Fibrosis-Net initiative for pulmonary fibrosis progression prediction, and Cancer-Net initiative for cancer screening.

MIMIC-ED is a large, freely available database of emergency department (ED) admissions at the Beth Israel Deaconess Medical Center between 2011 and 2016. 448,972 ED stays with vital signs, triage information, medication reconciliation, medication administration, and discharge diagnoses available

Chest X-ray dataset with eye tracking and report dictation. Built on MIMIC Chest X-ray dataset.1,083 CXR images.

Related publication:

Karargyris, A., Kashyap, S., Lourentzou, I. et al. Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development.Sci Data 8, 92 (2021).

This database is the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset.Annotated by three radiologists with majority vote adjudication by board certified radiologists,RICORD consists of 240 thoracic CT scans and 1,000 chest radiographs contributed from four international sites.

A comprehensive dataset of 6,388 surgical patients composed of intraoperative biosignals and clinical information from the Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Korea .

The NuCLS dataset contains over 220,000 labeled nuclei from breast cancer images from The Cancer Genome Atlas( TCGA). These nuclei were annotated through the collaborative effort of pathologists, pathology residents, and medical students.

CheXpert is a  public dataset for chest radiograph interpretation, consisting of 224,316 chest radiographs of 65,240 patients from Stanford Hospital.

The GDC Portal is a platform from National Cancer Institute(NCI) with cancer related genomic data for 80,000+ cases.

Annotations by CARING’s (Centre for Advanced research in Imaging, Neuroscience and Genomics) expert radiologists on COVID-19 positive X-rays.The corresponding X-rays were released by Medical Imaging Data Bank of the Valencia region (BIMCV).

BIMCV-COVID19+ dataset is a large dataset with chest X-ray images  and computed tomography (CT) imaging of COVID-19 patients along with their radiographic findings, pathologies, polymerase chain reaction (PCR), immunoglobulin antibody tests and radiographic reports from Medical Imaging Databank in Valencian Region Medical Image Bank (BIMCV).These iterations of the database include 7377 CR, 9463 DX and 6687 CT studies.

Provided on Kaggle by the Vingroup Big Data Institute (VinBigData) aims to promote fundamental research and investigate novel and highly-applicable technologies.A dataset consisting of 18,000 images that have been annotated by experienced radiologists.

The EchoNet-Dynamic database includes 10,030 labeled echocardiogram videos and human expert annotations (measurements, tracings, and calculations) to provide a baseline to study cardiac motion and chamber sizes.

Related publication: Video-based AI for beat-to-beat assessment of cardiac function

941 sentences from 911 PubMed abstracts, annotated with PGx entities of interest (mainly genes variations, gene, drugs and phenotypes), and relationships between those.

40+ speciality classified list of open source datasets for healthcare with direct links to the datasets and more information.

More than a 100 healthcare related datasets from around the world, classified and annotated.

Dataset created for the purpose of continuing research into COVID-19. However with information from all 50 states and the District of Columbia, many US statistics can be compared.

The DILIrank dataset is an updated version of the LTKB Benchmark dataset. DILIrank consists of 1,036 FDA-approved drugs that are divided into four classes according to their potential for causing drug-induced liver injury (DILI).

Dataset for automatically segmenting and classifying corneal ulcers with 712 ocular staining images and the associated segmentation labels for flaky corneal ulcers.

4000+ healthcare datasets made available from Harvard University.Searchable and diverse.

Semi automatically generated nuclei instance segmentation and classification dataset with exhaustive nuclei labels across 19 different tissue types. The dataset consists of 481 visual fields, of which 312 are randomly sampled from more than 20K whole slide images at different magnifications, from multiple data sources.

A dataset with Images,mainly Chest X-rays from COVID-19 patients.

Multiple data sources for COVID-19 in a unified data model, ready for analysis at one place.

In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19). CORD-19 is a resource of over 29,000 scholarly articles, including over 13,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses.

This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus.

The data is available since 22 Jan, 2020.

Largest collection of Intracranial hemorrhage CT scans.874 035 images with expert annotations.

Reference: Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge

One of the most comprehensive source of many datasets in healthcare.Primarily from ICU patients.

https://physionet.org/about/database/

MIMIC – IV Dataset (https://physionet.org/content/mimiciv/0.4/)

Includes:

• Clinical datasets such as MIMIC,eICU collaborative and Pediatic ICU datasets.
• Waveform datasets with ECG,EEG,arterial blood pressure waveform.
• ECG datasets with various pathophysiologic changes and drug interactions.
• Fetal datasets including sounds and ECG.
• Gait and Balance datasets include gait dynamics for patients with various neurodegenerative disorders.
• Neuro and Myoelectic datasets with EEG,EMG and evoked potential waveforms.
• Image datasets with Chest X-rays and MRI images.
• Computed Tomography Images for Intracranial Hemorrhage Detection and Segmentation
• Miscellaneous datasets with text, language,posture and other datasets

Alzheimer’s disease patient’s imaging(MRI), clinical, genomic, and biomarker data for the purposes of scientific investigation, teaching, or planning clinical research studies.

http://adni.loni.usc.edu/data-samples/access-data/

RIM-ONE is a database for optic disc and cup segmentation evaluation by Medical Image Analysis group.

Contains data related to 23,376 intensive care unit and high dependency unit admissions at Amsterdam University Medical Center of adult patients from 2003-2016.

The FDA Adverse Event Reporting System (FAERS) is a database that contains adverse event reports, medication error reports and product quality complaints resulting in adverse events that were submitted to FDA

A repository of segmented cells from the thin blood smear slide images from the Malaria Screener research activity.The dataset contains a total of 27,558 cell images with equal instances of parasitized and uninfected cells.

1200 Retinal image dataset with annotation.

A de-identified dataset of retinal fundus images for glaucoma analysis (RIGA) derived from three sources with 750 original images and 4500 manual marked images

The public database contains 15 images of healthy patients, 15 images of patients with diabetic retinopathy and 15 images of glaucomatous patients.

39 images for development of vessel extraction algorithms suitable for retinal screening programmes.

Datasets from National Cancer Institute of over 54000 patients. They include data on participant characteristics, screening exam results, diagnostic procedures, lung cancer, and mortality. Images from over 75,000 CT screening exams are available. Over 1,200 pathology images from a subset of NLST lung cancer patients (~500 of over 2,000 patients) may be viewed.

Polysomnography dataset from NSRR for sleep studies.Large collection of deidentified physiologic signals perfect for ML development.

A large collection of multi-source dermatoscopic images of common pigmented skin lesions containing 10000 images.

Related publication:The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

This open source repository has more than 400 datasets including healthcare(100+) and non-healthcare ones in searchable and categorized format.

CMS datasets provide US Medicare and Medicaid datasets.

ResDAC(The Research Data Assistance Center) provides free support to users of CMS datasets.Link:  https://www.resdac.org/learn

Center for Disease Control’s datasets.Useful for incidence,prevalance of various disorders and mortality data from across the US.

Agency for Healthcare Research and Quality’s HCUP datasets used to identify, track, and analyze US national trends in health care utilization, access, charges, quality, and outcomes.

UK government’s National Health services datasets.NHS choices datasets are useful for NLP and sentiment analysis both for GPs and hospitals.

Brain MRI datasets from Open Accesss series of Imaging Studies(OASIS).

Cancer epidemiology data available through NCI’s Surveillance,Epidemiology and End Result Program(SEER).

Cancer and genomics datasets.

A dataset of 14,000+ anonymized, radiologist labeled musculoskeletal X-rays from 12,000+ patients from Stanford ML group.

Related publication: https://arxiv.org/abs/1712.06957

1500+ knee MRI anonymized dataset from NYU.

One stop to learn Natural Language processing and more.

Related publication: Bird, Steven, Edward Loper and Ewan Klein (2009), Natural Language Processing with Python. O’Reilly Media Inc.

An excellent resource for trends and updates in AI, especially NLP by Elvis Saravia.

An excellent collection of articles on data science.

Google’s powerful search engine to assist with dataset search.

Over 100,000 anonymized chest x-ray images and their corresponding data from more than 30,000 patients, including many with advanced lung disease.

NIH release of  a dataset containing 32,000 CT scan images with annotated lesions  belonging to 4400 unique patients.

A CMS initiative to democratize research and development using beneficiary data.Greater than 70 million patient dataset available.

The link below is for NIH’s strategic plan for data science in healthcare.A must read for anyone using data in healthcare for research and innovation

Largest open source Chest X-Ray data set available through NIH’s clinical center.See the link in the article to access the data.Also available through GITHUB and KAGGLE.

One of the the largest and most advanced software development platform in the world with many datasets and repositories.

Kaggle is a great resource for de-identified datasets in healthcare.

A biomedical data search engine which searches for datasets across registries.

A place to store, share or find data.A platform for biomedical  research.

Detailed data repositories for biomedical research especially proteins and genetics.