Search Results for author:
Found 5 papers, 1 papers with code
On the calibration of neural networks for histological slide-level classification
We observe that Transformers lead to good results in terms of classification performance and calibration.
Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study
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 diagnosing melanomas.
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma
To evaluate this system, we conducted a three-part reader study to assess its impact on clinicians' diagnostic accuracy, confidence, and trust in the XAI-support.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images
We show that our method proves to be multi-domain capable, provides the highest image quality among the compared methods, and can most reliably fool the domain classifier while keeping the tumor classifier performance high.
Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise
We conduct our experiments on tile-level under the aspects of domain shift and label noise, as well as on slide-level.
