QCNN – Quantum-enhanced AI for Medical Diagnostics
Are you ready for better and faster diagnoses in medical imaging? Our hybrid algorithms leverage the power of quantum technologies to improve AI algorithms and to overcome unique challenges of medical imaging, particularly the scarcity of data. At the Fraunhofer Institute for Cognitive Systems IKS, we specialize in the development of quantum-enhanced convolutional neural networks (QCNNs), which have the potential to lead to more powerful AI algorithms even with limited training data sets.
Challenge: Data scarcity for Training AI in Medicine
One of the biggest hurdles in applying AI is the need for extensive training data for effective training. The high costs of medical imaging procedures, expert annotations, and privacy concerns often limit the availability of sufficient data sets, especially for rare diseases. This creates an urgent need for innovative solutions that can make effective use of limited data sets. This requires ensuring high accuracy, developing trustworthy algorithms, and overcoming the complexity of medical imaging.
Solution approach: Quantum-enhanced AI Algorithms
Our approach employs hybrid quantum-classical convolutional neural networks (QCCNNs) that leverage quantum kernels to identify more complex patterns in medical images than it is the case for classical CNN algorithms. By integrating quantum subroutines into classical compute workflows, we can effectively combine quantum and classical compute resources to eventually address challenges in medical diagnostics, especially when image data is limited.
Key Benefits:
- Opportunity for improved Diagnostic Accuracy: by identifying and classifying tumors with small amounts of data.
- Efficient Data Utilization: Overcoming data scarcity through quantum-enhanced AI algorithms.
- Robust performance: Hybrid models demonstrate competitive performance compared to fully classical models.
Insight Background
We want to strengthen the use of AI in medical imaging through quantum technologies. The hybrid quantum-classical convolutional neural networks (QCCNN) promise to interpret patterns in medical data not accessible to classical computers. We apply this hybrid algorithm, for example, to classify tumors in breast ultrasound images, among other things. When comparing our algorithm with a fully classical model, the performance of the hybrid algorithm was similar or, in some cases, even better. Building on this research, we are now working on applying these algorithms in clinical settings.
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