Africa needs 1 radiologist per 40,000 people but has 1 per 1 million. Europe and North America face similar workforce pressures. Can deep learning bridge this gap without requiring million-dollar infrastructure? This talk presents award-winning research (Q1 Springer publication, Best Presenter Award NexSymp 2025) demonstrating how to build and deploy automated radiology report generation systems on consumer-grade hardware—designed for real-world constraints, not research fantasy. THE TECHNICAL STACK: - CNN Architectures: ResNet-50 and DenseNet-121 for chest X-ray feature extraction - Transformer-based Generation: Attention mechanisms for clinical report generation - Multi-modal Fusion: Combining visual features with patient metadata - Performance: 0.347 BLEU-4, 0.289 ROUGE-L on benchmark datasets - Infrastructure: Single GPU deployment (<60 second inference) - Training: ~10,000 image-report pairs (achievable in resource-constrained settings) FROM RESEARCH TO PRODUCTION: Unlike academic projects requiring massive compute, this solution runs on ~$1,500 hardware: - Single consumer-grade GPU (RTX 3090 or similar) - Standard server infrastructure - Open-source frameworks (PyTorch, Transformers) - Reduces radiologist report time by 90% (10-15min → 60sec) CRITICAL CHALLENGES SOLVED: - Data Scarcity: Strategies for training with limited medical datasets - Model Interpretability: Building clinical trust through explainable AI - Bias Detection: Ensuring fairness across diverse patient populations - Regulatory Compliance: Navigating medical AI deployment requirements - Production Deployment: Moving from Jupyter notebooks to production systems CODE DEMONSTRATIONS: The talk includes: - Pre-written PyTorch code examples with detailed walkthroughs - Real-time model inference demonstrations on sample X-rays - Live demo of report generation (<60 second predictions) - Attention mechanism visualizations showing what the model "sees" - Docker deployment architecture - All code will be made available in a GitHub repository REAL-WORLD IMPACT: This isn't theoretical—the approach is validated through: - Q1 journal publication (Discover AI - Springer) - International conference presentation (NexSymp 2025, Malaysia) - Best Presenter Award recognition - Featured in Scienmag Science Magazine TAKEAWAYS: Attendees will leave with: 1. Practical architecture for medical AI systems 2. Strategies for resource-efficient deep learning deployment 3. Framework for clinical validation and trust-building 4. Code patterns for production medical AI 5. Understanding of ethical considerations in healthcare AI Perfect for developers, ML engineers, and data scientists interested in applying AI to high-stakes real-world problems especially those working in startups, NGOs, or resource-constrained environments.

Session Speakers

David Agbolade

Senior Data Scientist, | AI Researcher | SheerFit Founder