- Data Categories
Agriculture Data 
B2B Data 
Biometric Data 
Company Data 
Conservation Data 
Demographic Data 
Economic Data 
Environmental Data 
Geospatial Data 
Healthcare Data 
Individual Data 
Industry-Specific Data 
Internet of Things (IoT) Data 
Legal and IP Data 
People and Employment Data 
Political Data 
Psychology Data 
Real Estate Data 
Retail & Commerce Data 
Social Data 
Space Exploration Data 
Sports & Entertainment Data 
Web Data
- Data Categories
- Agriculture Data
- B2B Data
- Biometric Data
- Company Data
- Conservation Data
- Demographic Data
- Economic Data
- Environmental Data
- Geospatial Data
- Healthcare Data
- Individual Data
- Industry-Specific Data
- Internet of Things (IoT) Data
- Legal and IP Data
- People and Employment Data
- Political Data
- Psychology Data
- Real Estate Data
- Retail & Commerce Data
- Social Data
- Space Exploration Data
- Sports & Entertainment Data
- Web Data
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
Connected Datasets
Data Collection by Shaip: Text, Audio, Image, Video for AI & ML Training
ShAIp’s dataset – ‘Data Collection by Shaip: Text, Audio, Image, Video for AI & ML Training’ provides Medical Imagery Data, Patient Data, Retail & Commerce Data and Healthcare Data that can be used in , V2X Technology and AI Chatbots
Shaip – Medical Image Data (2Mn+ medical images, 31 different specialties)
ShAIp’s dataset – ‘Shaip – Medical Image Data (2Mn+ medical images, 31 different specialties)’ provides Medical Imagery Data and Patient Data that can be used in
Data Annotation / Data Labeling by ShAIp: Text Labeling, Image Labeling, Audio Labeling, Video Labeling for AI & ML
ShAIp’s dataset – ‘Data Annotation / Data Labeling by ShAIp: Text Labeling, Image Labeling, Audio Labeling, Video Labeling for AI & ML’ provides , Medical Imagery Data and EHR Data that can be used in