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Detecting Domain Shift in Multiple Instance Learning for Digital Pathology Using Fréchet Domain Distance
Our contributions include showing that MIL for digital pathology is affected by clinically realistic differences in data, evaluating which features from a MIL model are most suitable for detecting changes in performance, and proposing an unsupervised metric named Fr\'echet Domain Distance (FDD) for quantification of domain shifts.
Generalisation effects of predictive uncertainty estimation in deep learning for digital pathology
In this work we evaluate if adding uncertainty estimates for DL predictions in digital pathology could result in increased value for the clinical applications, by boosting the general predictive performance or by detecting mispredictions.
Unsupervised anomaly detection in digital pathology using GANs
Machine learning (ML) algorithms are optimized for the distribution represented by the training data.
Survey of XAI in digital pathology
We present a survey on XAI within digital pathology, a medical imaging sub-discipline with particular characteristics and needs.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
