With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

Digital pathology has transformed the traditional pathology practice of analyzing tissue
under a microscope into a computer vision workflow. Whole-slide imaging allows …

Cancer diagnosis, prognosis, mymargin and therapeutic response predictions are based on
morphological information from histology slides and molecular profiles from genomic data …

Cancer prognostication is a challenging task in computational pathology that requires
context-aware representations of histology features to adequately infer patient survival …

Cell detection is a fundamental task in computational pathology that can be used for
extracting high-level medical information from whole-slide images. For accurate cell …

With the remarkable success of representation learning for prediction problems, we have
witnessed a rapid expansion of the use of machine learning and deep learning for the …

Deep Learning-based computational pathology algorithms have demonstrated profound
ability to excel in a wide array of tasks that range from characterization of well known …

Digital pathology allows computerized analysis of tumor ecosystem using whole slide
images (WSIs). Here, we present single-cell morphological and topological profiling (sc …

The increasing adoption of the whole slide image (WSI) technology in histopathology has
dramatically transformed pathologists' workflow and allowed the use of computer systems in …