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What Is Medical Imagery Data?

Images of normal, diseased, and injured body parts make up medical imagery data. These images are of many different types and taken with many different kinds of equipment.

Where Does Medical Imagery Data Come From?

Health care professionals and researchers generate medical images using many different types of machines and techniques. For example, PET scans, MRIs, radiographics, ultrasounds, DEXA scans, CT scans, theranostic radiotracers, and so on.

What Types of Columns/Attributes Should I Expect When Working with This Data?

Common attributes of medical image data include the machine and technique used, the disease or injury recorded, the organ or body part pictured, and patient information. Of course, medical professionals keep this patient information anonymous.

What Is This Data Used For?

The main purpose of this data is to diagnose and treat patients.

Subsequent to this, researchers, medical students, and other individuals use the images to study or research medical conditions.

How Should I Test the Quality of Medical Imagery Data?

The lack of universal standards for annotating and recording image data make quality testing difficult. However, the plethora of sources for good quality anonymized medical images make building a valuable dataset relatively easy. Therefore, to build your own dataset, simply focus on relevant images and take care to cleanse and standardize the data.

Interesting Case Studies and Blogs to Look Into

Science Direct: Medical Image Data – an overview
Github: A list of Medical imaging datasets.

Tangible Examples of Impact

Capturing clear and complete images of physical structures can be challenging for certain populations, including children, the obese, and individuals with physical impairments as well as those with anxiety, dementia, or claustrophobia.

Advanced imaging techniques and personalized protocols for imaging acquisition, supported by machine learning, can ensure that providers can reduce patient stress while still capturing the necessary data for diagnostics and care.

Health IT Analytics: Medical Imaging, Machine Learning to Align in 10 Key Areas

Relevant datasets

Graticule Life Sciences

by Graticule-logo

Graticule life sciences has data sets from clinical notes, images and non clinical data for research and studies.

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Graticule Medical Devices

by Graticule-logo

Graticule Medical Devices provide data sourced from EHR records to improve biomarker discovery and algorithm training for robotic surgery and other medical advances

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EMIS Health EMIS Web

by EMIS-logo

EMIS Health EMIS Web allows healthcare providers, community care services and hospitals to share expertise and information between their varying areas improving customer care and safety.

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