Prateek Pani supervised by Dr. Girish Varma received his Master of Science by Research  in Computer Science and Engineering (CSE). Here’s a summary of his research work on Application of Domain Adaptation and Active Learning in Ophthalmology and Quality Testing of food grains:

This thesis explores the transformative potential of artificial intelligence (AI) in healthcare, particularly through enhancing diagnostic accuracy in medical imaging and streamlining data annotation processes.

Central to this thesis is the development of an AI system using Unsupervised Domain Adaptation (UDA) to accurately grade retinal development in pediatric patients across different optical coherence tomography (OCT) devices. This innovative approach overcomes device-specific limitations, offering a robust and generalisable classification model without the need for device-specific data, matching the diagnostic accuracy of experienced clinicians.

Additionally, the thesis addresses the variability in fundus image interpretation among medical professionals by employing a novel annotation tool. This tool facilitates a variability study among medical graders, underscoring the necessity for AI systems to account for such variability to enhance diagnostic reliability.

This thesis also delves into the application of Batch Active Learning (BAL) for optimizing seed grading mechanisms in agriculture. By systematically selecting and labeling the most informative samples, the research demonstrates a significant improvement in the model’s accuracy while reducing the time and cost associated with manual data annotation. The integration of BAL with advanced machine vision techniques highlights its potential to revolutionize agricultural practices, particularly in enhancing the efficiency and accuracy of seed quality classification. This contribution underscores the versatility and scalability of AI-driven approaches in both healthcare and agriculture.

Overall, the thesis provides a comprehensive evaluation of Al’s capability to handle domain shift-a significant challenge when training data differs from deployment data. It offers a framework for future models to be device-agnostic, enhancing applicability across different devices and settings. The findings not only contribute to the field of medical diagnostics but also offer insights into the broader application of AI in real-world clinical and agricultural settings.

December 2024