Medical & Healthcare Sector
Medical and healthcare sectors utilize Computer Vision to markup radiological scans such as X-rays, CT scans, and MRIs, enhancing AI-based diagnosis and therapy development.
Computer Vision enables AI models to automatically identify significant data points from visual content found in picture and video data as well as physical environments. This technology enables operations through the following functions:




The achievement of these results requires data annotation with high-quality standards. The expert team at Logictive Solutions optimizes the training process of AI models to help businesses develop effective and precise CV applications.
The company delivers tailored annotation services that fulfil precise AI training demands and business needs:
Keypoint annotation involves marking specific points on an object, like joints on a human body or corners on a face. It's widely used in pose estimation and facial recognition tasks.
These points help train models to understand shapes, motions, and spatial relationships between features, making it essential for applications like activity recognition and gesture tracking.

The technological advancement of industries occurs through Computer Vision, enabling artificial intelligence to automate procedures while extracting meaningful insights from collected data.
Medical and healthcare sectors utilize Computer Vision to markup radiological scans such as X-rays, CT scans, and MRIs, enhancing AI-based diagnosis and therapy development.
AI systems require annotated data to detect roads, traffic elements, and human beings for safe autonomous vehicle navigation and operation.
Computer Vision technology enhances product discovery and optimizes inventory management and retail surveillance systems for loss prevention in the Retail & E-commerce industries.
Facial recognition, along with anomaly detection technologies powered by artificial intelligence, enhances security in public domains, spaces, and workplaces, including confined areas.
High-precision inspection systems created by AI enable manufacturing plants to apply advanced quality control measures, identifying product defects while performing automated checks.
Annotation plays a key role in training computer vision models by providing labeled examples the AI can learn from. It helps the model understand what to look for in images or videos by linking visual data with meaningful tags.
Annotation provides the ground truth needed to teach AI models how to identify and interpret visual patterns. Without accurately labeled data, the models cannot learn effectively or perform tasks like object detection or image classification.
Annotation is typically done by trained data labelers or annotation specialists using specialized tools. In some cases, it may be partially automated and then manually verified for accuracy.