Bridging Quantum Computing and Deep Learning for Intelligent Medical Diagnosis

The convergence of quantum computing and artificial intelligence holds transformative potential for the future of medical diagnostics. This expert lecture will explore how hybrid quantum-classical models are revolutionizing the analysis of complex medical images, with a special focus on brain tumor and retinal disease detection. The session will begin with an overview of classical deep learning techniques used in medical image classification and segmentation, followed by an introduction to quantum computing fundamentals.

We will then delve into the design of hybrid architectures that fuse CNN and Vision Transformer backbones with quantum layers using frameworks like PennyLane and AWS Braket. Real-world case studies—including brain tumor detection using MRI data and diabetic retinopathy grading using fundus images—will demonstrate the practical implications and performance benefits of quantum-enhanced models. Additionally, statistical feature integration and explainability techniques (e.g., Grad-CAM) will be covered to highlight the interpretability of such models in clinical applications.

This talk aims to inspire students and researchers to adopt cutting-edge quantum machine learning tools for next-generation healthcare solutions.