Fully automatic deep learning has become the state-of-the-art technique for many tasks
including image acquisition, analysis and interpretation, and for the extraction of clinically …

In this white paper, experts from the Digital Pathology Association (DPA) define terminology
and concepts in the emerging field of computational pathology, with a focus on its …

Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative
analysis of imaging data for biological and biomedical applications. Many bioimage analysis …

Active learning is a powerful tool when labelling data is expensive, but it introduces a bias
because the training data no longer follows the population distribution. We formalize this …

Tissue-level semantic segmentation is a vital step in computational pathology. Fully-
supervised models have already achieved outstanding performance with dense pixel-level …

Abstract Purpose of Review Our goal is to provide an overview of machine learning methods
and artificial intelligence in digital pathology image analysis. We also highlight novel …

BACKGROUND Artificial intelligence, such as convolutional neural networks (CNNs), has
been used in the interpretation of images and the diagnosis of hepatocellular cancer (HCC) …

Biomedical imaging Is an important source of information in cancer research.
Characterizations of cancer morphology at onset, progression, and in response to treatment …

Deep learning models have been successfully used in medical image analysis problems but
they require a large amount of labeled images to obtain good performance. However, such …

Deep neural networks, in particular convolutional networks, have rapidly become a popular
choice for analyzing histopathology images. However, training these models relies heavily …