Unlocking the potential of quantum computing for cancer detection

How to design and use quantum neural networks?

Artificial intelligence increases in importance in the medical diagnostics, like in the diagnosis of cancer. However, in this critical context, accurate and reliable predictions are crucial. Training of machine learning algorithms typically requires large, annotated datasets, especially for computer vision tasks. Clinical studies typically achieve sample sizes of around 100 to 1000, which is often not sufficient for ML approaches. Quantum computing assisted algorithms promise to achieve high prediction accuracy even with limited amounts of data.

In the first part of this online seminar Dr. Balthasar Schachtner from the clinical data science department of the LMU will introduce the challenges in using AI methods in the clinical context, with a focus on radiology. In the second part, scientists from the Fraunhofer IKS then illustrate how the challenges could be tackled in perspective via quantum computing, using here examples from classification of breast cancer or nodules in the lung.

Highlights

• Introduction the difficulties of using AI in a clinical context by one expert from the field
• Introduction to the emerging technology of quantum computing
• Glimpse into current research: Can quantum computing help us with the challenges we face in the clinical context?
   

Target audience

• General audience interested in technology
• AI experience helpful
• especially appropriate for any company interested in anomaly detection
   

Agenda