Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic
differences among a variety of cell populations. It paves the way to studying biological …

Deep learning of artificial neural networks has become the de facto standard approach to
solving data analysis problems in virtually all fields of science and engineering. Also in …

Background: Deep learning (DL) is a representation learning approach ideally suited for
image analysis challenges in digital pathology (DP). The variety of image analysis tasks in …

Conventional methods for intraoperative histopathologic diagnosis are labour-and time-
intensive, and may delay decision-making during brain-tumour surgery. Stimulated Raman …

Measuring the morphological parameters of galaxies is a key requirement for studying their
formation and evolution. Surveys such as the Sloan Digital Sky Survey have resulted in the …

This paper proposes a new approach to automatically quantify the severity of knee
osteoarthritis (OA) from radiographs using deep convolutional neural networks (CNN) …

This paper describes the sequence of construction of a cell nuclei classification model by the
analysis, the characterization and the classification of shape and texture. We describe first …

Access to primary research data is vital for the advancement of science. To extend the data
types supported by community repositories, we built a prototype Image Data Resource …

This paper presents an overview of the image analysis techniques in the domain of
histopathology, specifically, for the objective of automated carcinoma detection and …

A framework for automated detection and classification of cancer from microscopic biopsy
images using clinically significant and biologically interpretable features is proposed and …