Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …

With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …

The rapid development of precision medicine in recent years has started to challenge
diagnostic pathology with respect to its ability to analyze histological images and …

With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …

Background Due to their ability to solve complex problems, deep neural networks (DNNs)
are becoming increasingly popular in medical applications. However, decision-making by …

The success of convolutional neural networks (CNNs) in various applications is
accompanied by a significant increase in computation and parameter storage costs. Recent …

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens
highly depend on the phenotype and topological distribution of constituting histological …

Within the last decade, neural network based predictors have demonstrated impressive-and
at times superhuman-capabilities. This performance is often paid for with an intransparent …

The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based
detection of mitotic nuclei is a significant overhead and necessitates automation. This work …

Histopathology tissue analysis is considered the gold standard in cancer diagnosis and
prognosis. Whole-slide imaging (WSI), ie, the scanning and digitization of entire histology …