42 citations · 50 across the 4 of their papers we have counts for
5 papers
Validation of Conformal Prediction in Cervical Atypia Classification
Misgina Tsighe Hagos, Antti Suutala, Dmitrii Bychkov +6
Deep learning based cervical cancer classification can potentially increase access to screening in low-resource regions. However, deep learning models are often overconfident and d…
Detecting Domain Shift in Multiple Instance Learning for Digital Pathology Using Fréchet Domain Distance
Milda Pocevičiūtė, Gabriel Eilertsen, Stina Garvin +1
Multiple-instance learning (MIL) is an attractive approach for digital pathology applications as it reduces the costs related to data collection and labelling. However, it is not c…
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…
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…
Survey of XAI in digital pathology
Milda Pocevičiūtė, Gabriel Eilertsen, Claes Lundström
Artificial intelligence (AI) has shown great promise for diagnostic imaging assessments. However, the application of AI to support medical diagnostics in clinical routine comes wit…