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.
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.
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.
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.
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.
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.
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’s dataset – ‘Shaip – Medical Image Data (2Mn+ medical images, 31 different specialties)’ provides Medical Imagery Data and Patient Data that can be used in
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