Center for Visual Information Technology, IIIT Hyderabad - Clinical Deep Learning Engineer

December 2020 | Hyderabad, India

The Medical AI Mission

This was a fascinating month-long intensive project where I got to work at the intersection of cutting-edge AI and life-saving healthcare. Based at IIIT Hyderabad’s renowned Center for Visual Information Technology, I was part of a team developing AI systems that could help doctors make better, faster diagnoses.

Working with Real Doctors

One of the most rewarding aspects was collaborating directly with 5 practicing doctors to understand their real-world challenges. These weren’t just theoretical discussions - we were solving actual problems that doctors face every day when analyzing medical images. It was like being a translator between the language of medicine and the language of machine learning.

The Mammography Breakthrough

The crown jewel of this project was developing an AI system for mammogram analysis that achieved 97.86% accuracy - a result that even impressed the clinical team! Using weakly-supervised segmentation with U-Net architecture, we created a system that could not only detect potential issues but also explain its reasoning to doctors.

Beyond Just Accuracy - Explainable AI

What made this project special wasn’t just the high accuracy - it was creating AI that doctors could actually trust and understand. The system provided explainable predictions with multi-class classification and precise segmentation of affected regions. Doctors could see exactly what the AI was “looking at” and why it made specific recommendations.

The Technical Journey

Working with CNNs, PyTorch, and TensorFlow on very large, unstructured medical datasets was like solving a massive puzzle. Each dataset brought its own challenges - different image qualities, varying patient demographics, and the constant need to maintain the highest standards of accuracy because lives could depend on it.

Multi-Modal Medical Analysis

The project wasn’t limited to just mammograms. We also worked on:

  • X-ray analysis for various conditions
  • Fetal ultrasound scans with landmark detection
  • Data creation for training robust medical AI systems

The Research Impact

This intensive work resulted in a published paper that has been cited by researchers worldwide. The combination of high accuracy with explainable AI made it a reference point for medical AI applications, proving that you can have both performance and interpretability.

User Research in Healthcare

Performing user research with doctors taught me that the best AI systems are designed with end-users in mind. It wasn’t enough to build a technically impressive model - it had to fit into real clinical workflows and actually help doctors do their jobs better.

The GPU Adventures

Training deep learning models on very large unstructured datasets using GPUs was like conducting a high-performance orchestra. Each model training session was an exercise in patience, optimization, and resource management - but the results were worth every late night spent monitoring training progress.

The Clinical Validation Process

Working in the medical field taught me about the rigorous validation processes required for healthcare AI. Every prediction had to be scrutinized, every edge case examined, and every decision justified. It was a masterclass in responsible AI development.

Key Achievements

🏆 Technical Excellence

  • 97.86% accuracy in mammogram analysis
  • Explainable AI with visual reasoning
  • Multi-class classification and segmentation
  • Robust performance across diverse datasets

🩺 Clinical Impact

  • Doctor collaboration for real-world relevance
  • Published research with practical applications
  • Evidence-based predictions for clinical use
  • Improved diagnostic assistance for radiologists

🔬 Research Contribution

  • Published paper in international conference
  • Open-source contributions to medical AI
  • Validation methodologies for healthcare applications
  • Benchmark results for future research

🛠️ Technical Innovation

  • Weakly-supervised learning for medical imaging
  • U-Net architecture optimization
  • Feature engineering for medical data
  • Cross-modal analysis capabilities

This intensive project was a perfect blend of technical challenge and meaningful impact - exactly the kind of work that reminds you why AI can be such a powerful force for good in the world.