Artificial intelligence, deep convolutional neural networks, and deep learning are all niche
terms that are increasingly appearing in scientific presentations as well as in the general …

Computational cytology is a critical, rapid-developing, yet challenging topic in medical
image computing concerned with analyzing digitized cytology images by computer-aided …

Deep learning has achieved spectacular performance in image and speech recognition and
synthesis. It outperforms other machine learning algorithms in problems where large …

Identifying nuclei is often a critical first step in analyzing microscopy images of cells and
classical image processing algorithms are most commonly used for this task. Recent …

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays
in dynamic, living systems. A major critical challenge for this class of experiments is the …

Advances in optical microscopy, biosensors and cell culturing technologies have
transformed live cell imaging. Thanks to these advances live cell imaging plays an …

Deep learning has tremendous potential in single-cell data analyses, but numerous
challenges and possible new developments remain to be explored. In this commentary, we …

Computerized microscopy image analysis plays an important role in computer aided
diagnosis and prognosis. Machine learning techniques have powered many aspects of …

Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers
have to accomplish to assess the effects of different experimental conditions on biological …

Modern cytometry technologies present opportunities to profile the immune system at a
single-cell resolution with more than 50 protein markers, and have been widely used in both …