Member-only story
Neuro-Symbolic Hyperdimensional Learning for Breast Cancer Diagnosis: Uniting AI and Knowledge Graphs
Introduction
Artificial intelligence (AI) in healthcare has long promised to revolutionise diagnostic practices by enabling quicker, more accurate and interpretable decision-making systems. Traditional deep learning methods such as convolutional neural networks (CNNs) have gained immense popularity for their exceptional ability to detect complex patterns in medical images.
However, they are often criticized for their lack of transparency and explainability. As the demand for interpretable AI grows in critical fields such as healthcare, new paradigms that combine neuro-symbolic reasoning with hyperdimensional computing (HDC) are becoming more relevant. This article will dive deeply into how these two advanced AI methodologies can be fused into a cohesive framework for breast cancer diagnostics, discussing both the underlying mathematics and the benefits over traditional approaches.
We will break down the mathematical operations behind hyperdimensional computing, explain how knowledge graphs can be used to imbue symbolic reasoning into the learning process, and finally, compare this hybrid approach to standard AI methods.
