176 citations · 258 across the 8 of their papers we have counts for
5 papers · 1 filter
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-…