Recent progress in computational pathology has been driven by deep learning. While code
and data availability are essential to reproduce findings from preceding publications …

Computational pathology refers to applying deep learning techniques and algorithms to
analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led …

The interpretation of digitized histopathology images has been transformed thanks to
artificial intelligence (AI). End-to-end AI algorithms can infer high-level features directly from …

Make deep learning algorithms in computational pathology more reproducible and reusable |
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Histopathology images; microscopy images of stained tissue biopsies contain fundamental
prognostic information that forms the foundation of pathological analysis and diagnostic …

Deep learning techniques, such as convolutional neural networks (CNNs), generative
adversarial networks (GANs), and graph neural networks (GNNs) have, over the past …

Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …

Motivation Digital pathology supports analysis of histopathological images using deep
learning methods at a large-scale. However, applications of deep learning in this area have …

Deep learning algorithms have shown benefits for pathology in the context of risk
stratification of tumors. Although the results are promising, several steps have to be made to …

Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …