LUMID: Large-scale Unlabled Medical Imaging Dataset for Unsupervised and Self-supervised Learning

Osman, Islam

https://ror.org/03rmrcq20

University of British Columbia

https://orcid.org/0000-0001-9935-2417

Gupta, Anubhav

https://ror.org/03rmrcq20

University of British Columbia

Shehata, Mohamed S.

https://ror.org/03rmrcq20

University of British Columbia

Braun, John W.

https://ror.org/03rmrcq20

University of British Columbia

2024-12-04T20:59:50Z

2024-12-04T20:59:50Z

2024-12-04

doi:10.20383/103.01017

LUMID is a large-scale, unlabeled collection of over 2 million medical images spanning multiple imaging modalities, including CT scans, X-rays, MRIs, and more. This dataset has been meticulously curated from publicly available medical imaging repositories, addressing the critical challenge of limited scale in existing public datasets and the inaccessibility of high-quality private datasets. The primary motivation behind creating this dataset is to empower the medical imaging community with a resource suited for developing and training advanced deep learning models. By enabling the use of unsupervised and self-supervised learning approaches, this dataset facilitates the learning of rich, transferable representations that can significantly enhance performance across various medical imaging tasks, including classification, segmentation, and anomaly detection.

Key Features:
1) Diversity: Comprising images from multiple modalities and a wide range of medical imaging scenarios.
2) Scalability: A dataset of unprecedented size, providing a robust foundation for training deep neural networks.
3) Versatility: Specifically designed for unsupervised and self-supervised learning methods, fostering innovation in representation learning for medical imaging.
4) Open Access: Built entirely from public datasets, ensuring transparency and reproducibility.

This dataset is intended to serve as a cornerstone for advancing research in medical AI, fostering the development of models capable of generalizing across diverse imaging types and clinical conditions.

Federated Research Data Repository / dépôt fédéré de données de recherche

Creative Commons Attribution 4.0 International (CC BY 4.0)

https://creativecommons.org/licenses/by/4.0/

Medical imaging

CT

MRI

X-rays

LUMID: Large-scale Unlabled Medical Imaging Dataset for Unsupervised and Self-supervised Learning

f163c1b3-9c88-42f6-a7bb-5839ed6c4063

/1/published/publication_1012/

2024

Anubhav Gupta

Islam Osman

Mohamed S. Shehata

John W. Braun

Dataset

https://doi.org/10.7937/s17z-r072

https://doi.org/10.7937/TCIA.BBAG-2923

https://doi.org/10.7937/SPGK-0P94

https://doi.org/10.7937/p5k5-tg43

https://doi.org/10.7937/TCIA.2020.GQRY-NC81

https://doi.org/10.7937/VTW4-X588

https://doi.org/10.7937/tcia.2019.tt7f4v7o

https://doi.org/10.7937/TCIA.2019.WOSKQ5OO

https://doi.org/10.7937/K9/TCIA.2015.NPGZYZBZ

https://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU

https://doi.org/10.7937/K9/TCIA.2015.A6V7JIWX

https://doi.org/10.7937/K9/TCIA.2015.L4FRET6Z

https://doi.org/10.7937/k9/tcia.2015.u1x8a5nr

https://doi.org/10.7937/K9/TCIA.2015.NWTESAY1

https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX

https://doi.org/10.7937/K9/TCIA.2016.8LNG8XDR

https://doi.org/10.7937/K9/TCIA.2016.GKJ0ZWAC

https://doi.org/10.7937/tcia.2020.py71-5978

https://doi.org/10.7937/3CX3-S132

https://doi.org/10.7937/DJG7-GZ87

https://doi.org/10.7937/K9/TCIA.2016.V6PBVTDR

https://doi.org/10.7937/25T7-6Y12

https://doi.org/10.7937/k9/tcia.2018.oblamn27

https://doi.org/10.7937/K9/TCIA.2016.CX6YLSUX

https://doi.org/10.7937/K9/TCIA.2016.F7PPNPNU

https://doi.org/10.7937/K9/TCIA.2016.NDO1MDFQ

https://nihcc.app.box.com/v/ChestXray-NIHCC

RDF1020112

Computer and information sciences

Artificial intelligence (AI)

Computer vision in artificial intelligence

Informatique et systèmes d'information

Intelligence artificielle (IA)

Vision par ordinateur en intelligence artificielle