Model-Agnostic Meta-Learning for Digital Pathology

The performance of conventional deep neural networks tends to degrade when a domain shift is introduced, such as collecting data from a new site. Model-Agnostic Meta-Learning, or MAML, has achieved state-of-the-art performance in few-shot learning by finding initial parameters that adapt easily for new tasks. This thesis studies MAML in a digital pathology setting. Experiments show that a conventional model generalises poorly to data collected from another site. By annotating a few samples during inference however, a model with initial parameters obtained through MAML training can adapt to achieve better generalisation performance. It is also demonstrated that a simple transfer learning approach using a kNN classifier on features extracted from a conventional model yields good generalisation, but the variance caused by random sampling is higher. The results indicate that meta learning can lead to a lower annotation effort for machine learning in digital pathology while maintaining accuracy