OPTIMAM Mammography Image Database: a large scale resource of mammography images and clinical data

04/09/2020
by   Mark D Halling-Brown, et al.
0

A major barrier to medical imaging research and in particular the development of artificial intelligence (AI) is a lack of large databases of medical images which share images with other researchers. Without such databases it is not possible to train generalisable AI algorithms, and large amounts of time and funding is spent collecting smaller datasets at individual research centres. The OPTIMAM image database (OMI-DB) has been developed to overcome these barriers. OMI-DB consists of several relational databases and cloud storage systems, containing mammography images and associated clinical and pathological information. The database contains over 2.5 million images from 173,319 women collected from three UK breast screening centres. This includes 154,832 women with normal breasts, 6909 women with benign findings, 9690 women with screen-detected cancers and 1888 women with interval cancers. Collection is on-going and all women are followed-up and their clinical status updated according to subsequent screening episodes. The availability of prior screening mammograms and interval cancers is a vital resource for AI development. Data from OMI-DB has been shared with over 30 research groups and companies, since 2014. This progressive approach has been possible through sharing agreements between the funder and approved academic and commercial research groups. A research dataset such as the OMI-DB provides a powerful resource for research.

READ FULL TEXT
research
12/29/2022

Current State of Community-Driven Radiological AI Deployment in Medical Imaging

Artificial Intelligence (AI) has become commonplace to solve routine eve...
research
02/08/2022

The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.5M Screening and Diagnostic Mammograms

Developing and validating artificial intelligence models in medical imag...
research
07/01/2023

One Copy Is All You Need: Resource-Efficient Streaming of Medical Imaging Data at Scale

Large-scale medical imaging datasets have accelerated development of art...
research
09/20/2022

Generalisability of deep learning models in low-resource imaging settings: A fetal ultrasound study in 5 African countries

Most artificial intelligence (AI) research have concentrated in high-inc...
research
07/18/2023

Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and Education

The advancement of biomedical research heavily relies on access to large...
research
03/24/2023

Removing confounding information from fetal ultrasound images

Confounding information in the form of text or markings embedded in medi...

Please sign up or login with your details

Forgot password? Click here to reset