AI, as an emerging technology, has a tremendous potential to revolutionize healthcare industry.

It’s not enough to merely detect diseases; it’s important to be able to detect diseases early, rank them for risk and allow doctors to focus on prevention, prognosis and cure.


Quantitative analysis to give a detailed spatial interrogation, e.g. capturing nuclear orientation, texture, shape, architecture of the entire landscape and its most invasive elements from a standard hematoxylin and eosin (H&E) slide. Nuclei Segmentation, Epithelium Segmentation, Tubule Segmentation


An accurate and robust automated lymphocyte detection approach is of great importance in both computer science and clinical studies. Our detection process is based on image processing algorithms and machine learning methods to not only help in annotations, but also efficiently fine tunes.


The classification of microscopic biopsy images is the most challenging task for automatic detection of cancer from microscopic biopsy images. Classification provides the result of whether biopsy is benign or malignant using supervised machine learning approach.