15 citations · 30 across the 5 of their papers we have counts for
5 papers
Pitfalls of Conformal Predictions for Medical Image Classification
Hendrik Mehrtens, Tabea Bucher, Titus J. Brinker
Reliable uncertainty estimation is one of the major challenges for medical classification tasks. While many approaches have been proposed, recently the statistical framework of con…
Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer
Gesa Mittmann, Sara Laiouar-Pedari, Hendrik A. Mehrtens +63
The aggressiveness of prostate cancer, the most common cancer in men worldwide, is primarily assessed based on histopathological data using the Gleason scoring system. While artifi…
On the calibration of neural networks for histological slide-level classification
Alexander Kurz, Hendrik A. Mehrtens, Tabea-Clara Bucher +1
Deep Neural Networks have shown promising classification performance when predicting certain biomarkers from Whole Slide Images in digital pathology. However, the calibration of th…
Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise
Hendrik A. Mehrtens, Alexander Kurz, Tabea-Clara Bucher +1
In the past years, deep learning has seen an increase in usage in the domain of histopathological applications. However, while these approaches have shown great potential, in high-…
Improving robustness and calibration in ensembles with diversity regularization
Hendrik Alexander Mehrtens, Camila González, Anirban Mukhopadhyay
Calibration and uncertainty estimation are crucial topics in high-risk environments. We introduce a new diversity regularizer for classification tasks that uses out-of-distribution…