176 citations · 258 across the 8 of their papers we have counts for
8 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…
Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study
Tirtha Chanda, Sarah Haggenmueller, Tabea-Clara Bucher +19
Artificial intelligence (AI) systems have substantially improved dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing clinicians' c…
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…
Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study
Christoph Wies, Lucas Schneider, Sarah Haggenmueller +6
Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in dia…
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma
Tirtha Chanda, Katja Hauser, Sarah Hobelsberger +32
Although artificial intelligence (AI) systems have been shown to improve the accuracy of initial melanoma diagnosis, the lack of transparency in how these systems identify melanoma…
Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images
Martin J. Hetz, Tabea-Clara Bucher, Titus J. Brinker
The variation in histologic staining between different medical centers is one of the most profound challenges in the field of computer-aided diagnosis. The appearance disparity of…
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-…