Pathologists face a substantial increase in workload and complexity of histopathologic
cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic …

Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic
diagnosis of cancer is increasing as personalized cancer therapy requires accurate …

Applications of artificial intelligence and particularly deep learning to aid pathologists in
carrying out laborious and qualitative tasks in histopathologic image analysis have now …

Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers.
However, the growing number of these complex biomarkers tends to increase the cost and …

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

Deep learning (DL) is a powerful methodology for the recognition and classification of tissue
structures in digital pathology. Its performance in prostate cancer pathology is still under …

Deep learning refers to a set of computer models that have recently been used to make
unprecedented progress in the way computers extract information from images. These …

The tumor-immune microenvironment (TIME) encompasses many heterogeneous cell types
that engage in extensive crosstalk among the cancer, immune, and stromal components …

Deep Learning (DL) algorithms are a set of techniques that exploit large and/or complex real-
world datasets for cross-domain and cross-discipline prediction and classification tasks. DL …

Convolutional neural networks (CNNs) are state-of-the-art computer vision techniques for
various tasks, particularly for image classification. However, there are domains where the …