Machine learning may be the way of the future – but pathologists will remain a central component of artificially intelligent systems
Holger Lange and Cris Luengo | | Longer Read
At a Glance
- Machine learning is attracting increasing hype in the pathology sphere, especially in the realm of deep learning
- Deep learning has its pros and cons – as do other approaches to pathology AI, such as decision trees
- The key is to design a pathology-centric system that relies upon both human and computer input for full accuracy and effectiveness
- In the near future, such pathology-centric systems can assist us in providing better patient data, care, and outcomes
Most people have some understanding of artificial intelligence (AI) – but what exactly does it mean in the context of pathology? A pathology AI system is a computer program that assists pathologists in their work or provides automated pathology. The key capability of such a system is to analyze digital slide images using image analysis and “machine learning” – another buzzword! Machine learning is the process of learning a task (for instance, providing a diagnosis or a score) or a sub-task (such as classifying cells into different cell types) from data. There are many approaches in machine learning, including decision trees, random forests, and deep learning. You may have heard of some of these – in particular, the latter.
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