176 citations · 254 across the 7 of their papers we have counts for
7 papers
DNA Methylation Profiling in Melanoma: From Lesion Classification to Therapeutic Stratification
Jana T. Winterstein, Lukas Heinlein, Günter Raddatz +34
DNA methylation provides a stable record of cellular identity, capturing epigenetic programs that distinguish specialized cell states despite a shared genome. Because malignant tra…
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…
Using Multiple Dermoscopic Photographs of One Lesion Improves Melanoma Classification via Deep Learning: A Prognostic Diagnostic Accuracy Study
Achim Hekler, Roman C. Maron, Sarah Haggenmüller +28
Background: Convolutional neural network (CNN)-based melanoma classifiers face several challenges that limit their usefulness in clinical practice. Objective: To investigate the im…
Domain shifts in dermoscopic skin cancer datasets: Evaluation of essential limitations for clinical translation
Katharina Fogelberg, Sireesha Chamarthi, Roman C. Maron +2
The limited ability of Convolutional Neural Networks to generalize to images from previously unseen domains is a major limitation, in particular, for safety-critical clinical tasks…
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-…