Medical Image Annotation for AI in Healthcare and Deep Learning in Medicine
Use of healthcare training data for AI applications is giving a new dimension to medical science to utilize the power of machine learning for accurate disease diagnosis without human intervention. Cogito develops high-quality annotated medical datasets used to build and enhance various cutting-edge healthcare applications. It is offering medical image annotation for deep learning segmentation of medical image through AI models.
Applications of AI in Healthcare
Artificial intelligence is becoming more powerful and has enormous potential for the healthcare industry. Deep-learning technology is revolutionizing the operational process of healthcare industry inviting more opportunities for automation into various sub-fields. Applications of Artificial Intelligence in Healthcare are expanded into numerous subfields like diagnostic automation, treatment predictions, gene sequencing, and drug development that will drastically improve the quality, efficiency, and accessibility of entire healthcare sector.
AI-enabled systems are not going to completely replace human medical experts, rather they will enhance their capabilities and effectiveness by automating the most repetitive activities that are homogenous and prone to errors. AI-backed Machines use the computer vision to detect patterns and correlate the same with medical imaging data to identify possible diseases and prepare reports after analysis. X-Ray, CT Scan, MRI and other image-based test reports can be easily screened to predict various ailments. Cogito is helping the AI-enabled companies to create training data sets and develop cutting edge machine learning algorithms for healthcare industry.
AI Applications in Healthcare Industry:
- Managing Medical Records and Other Data
- Medical Imaging Analysis and Pattern Recognition
- Radiology – Diagnostic and reporting on X-Rays, CT Scans, MRIs
- Pathology – Assist pathologists in making rapid and accurate diagnoses
- Dermatology – Skin Image Analysis & Personalized Treatment
- Ophthalmology – Early detection of high-impact diseases like Glaucoma or Diabetic Retinopathy through medical image analysis
- Digital Consultation and Precision Medicine
- Treatment Design and Medication Management
- Health Monitoring and Virtual Patient Care
- Disease Management and Clinical Trials
- Drug Creation and Healthcare System Analysis
What is Healthcare Training Data?
Healthcare training data sets are required to train, develop and optimize machine learning algorithms. Quality of training data sets used significantly impacts the overall accuracy and efficacy of the algorithm used in developing AI-based applications. Access to high quality and accurate data sets is the first step towards building a successful AI product.
Why Healthcare Training Data is Important?
With limited access to healthcare data and patient data protection laws, its very hard to find high-quality medical data sets. Most companies have invested a substantial amount of resources in building the data sets in-house or through external sources. Cogito provides high-quality machine learning healthcare data sets at very affordable costs allowing end-users to focus on their core competency of building powerful AI applications.
How can Cogito Help with your Healthcare Training Data needs?
Cogito has established infrastructure to collect, classify and process machine learning healthcare data with the highest quality and maximum accuracy. Our curated training data sets are helping companies develop cutting-edge AI based healthcare applications and algorithms.
Cogito has a skilled on-demand workforce, with 10+ yrs. of experience capturing and enriching a wide variety of data types including speech, text, image, and video for machine learning business applications. Our flexible working models provide accuracy, scalability, elasticity and cost advantages to AI-oriented healthcare companies.
What Services does Cogito Offer for Healthcare Data Training?
Cogito has partnered with Machine Learning and AI companies to develop high-quality annotated medical data sets used to build and enhance various cutting-edge healthcare applications. Our certified medical experts can easily annotate all types of medical images and data with high accuracy and fast turnaround times. We are providing medical image annotation services with complete medical imaging solutions for the healthcare industry.
Medical Image Annotation for All Data Types
Medical images such as MRI, CT Scan or X-rays annotated for machine learning training in healthcare. All types of data sets are supported while annotating including DICOM and NIFTI formats to ensure the processing and originality of imaging data sets. Cogito offers world-class medical image annotation service for all types of data types with capability to convert the DICOM into desired formats like NIFTI by medical experts with micro levels of accuracy.
Types of Data Supported at Cogito for Medical Image Annotation
Cogito Offers following Services for Healthcare Industry:
- Generate high-quality, structured and de-identified medical datasets for use in the rapid creation of machine learning algorithms and computer vision systems.
- Our network of qualified Radiologists, Pathologists, Ophthalmologists, Dermatologists and General Physicians can.
- Review medical images to analyze patient X-rays, MRIs, CT, PET scans or other medical data.
- Accurately diagnose cases to teach next-generation deep learning products how to improve patient diagnosis accuracy.
- Provide Medical Insight & Feedback to help design tools that support deep learning products and services.
- Best-in-class accuracy through 1st and 2nd level verification to overcome human perception and bias (True Positives, False Positives and False Negatives).
- High throughput data labeling services without increasing costs or reducing quality.
- A dedicated and fully managed team works with you to Co-develop new AI modules with right sensitivity and specificity.
- Partner on specific case studies to support, coordinate and manage data studies with diverse patient populations.