47 citations · 64 across the 6 of their papers we have counts for
3 papers · 1 filter
Generalisation effects of predictive uncertainty estimation in deep learning for digital pathology
Milda Pocevičiūtė, Gabriel Eilertsen, Sofia Jarkman +1
Deep learning (DL) has shown great potential in digital pathology applications. The robustness of a diagnostic DL-based solution is essential for safe clinical deployment. In this…
Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications
Karin Stacke, Jonas Unger, Claes Lundström +1
Unsupervised learning has made substantial progress over the last few years, especially by means of contrastive self-supervised learning. The dominating dataset for benchmarking se…
Unsupervised anomaly detection in digital pathology using GANs
Milda Pocevičiūtė, Gabriel Eilertsen, Claes Lundström
Machine learning (ML) algorithms are optimized for the distribution represented by the training data. For outlier data, they often deliver predictions with equal confidence, even t…