Supporting Universities and Research Institutes in USA,
Denmark and UAE for their AI researches through our Annotation services

Research areas of the collaboration included Computer Vision, AI for Healthcare, Natural Language Processing and Smart Systems.

Challenge

There were number of significant problems for the academic research teams:

• A huge quantity of unstructured and complex data sets.
• Needing very precise and uniform annotations to provide legitimacy and credibility to research.
• Small in-house team for large scale labelling tasks.
• Short timelines that are correlated with academic publications and research deadlines.
• Detailed knowledge of domain-specific annotations needed in various areas of AI.

These challenges hindered both the training of the models and experimentation of models.

Our Solution

The structured and scalable annotation framework provided by Annotation Support addresses the needs of academic research.

Computer Vision Research

• Bounding box and polygon annotations for image datasets.
• Semantic Segmentation for object and scene understanding (grasp and detection).
• Video annotation for dynamic datasets (framing annotations).

Healthcare AI Research

• Medical image segmentation – MRI and CT scans.
• Classification and labelling of disease regions.
• Anonymisation of data and secure handling of data.

NLP & Text Research

• Text classification and sentiment labeling.
• Named Entity Recognition (NER) annotation.
• Structured data for language models.

Quality Assurance

• Use multiple levels of validation to assure the accuracy of the annotations.
• A uniform approach within the annotators.
• Specialists trained to use the domain for research purposes.
• Scales to large data sets.

Outcome

The partnership institutions had a good academic and research output as a result of collaboration:

• The high precision of the annotated quality was attained (95% above accuracy).
• Save time and effort for research teams in data set preparation.
• Shorter training timelines and experiments of AI models. Faster training and experiments of AI models.
• The quality of research output and the readiness of research to publication improved.
• Led to breakthroughs in several AI research fields.

Impact

Annotation Support turned unstructured and raw data into valuable, well-annotated and research-ready datasets, helping universities and research institutes in USA, Denmark and UAE to move on their AI innovation journey faster.

This allowed research teams to focus more on model development and experimentation, while we handled data preparation.

Conclusion

As an academic institution, Annotation Support remains focused on facilitating academic research worldwide, achieving high standards, reliability and scalable data annotation services. With our expertise, we can help you develop even more precise and effective AI systems based on solid data.