Transforming Pathology
Improving cancer diagnosis using Artificial Intelligence
We have developed state-of-the-art approaches for the interpretation of immunohistochemistry in whole slide images. We are working with research labs and pharma companies to provide accurate, cost effective and scalable solutions.
We aim to provide access to highly accurate pathological diagnosis at low cost in developing nations. We work with leading non-profit and government organizations to achieve this goal.
We are developing tools to enable the use of artificial intelligence in clinical diagnosis of cancer and other diseases leading to faster, more accurate and reproducible results.
Deep learning is an algorithmic technique that is revolutionizing what is possible in areas such as finance, communication, automotive, natural language processing, computer vision and more. It allows computers to analyze vast amounts of data and automatically detect patterns and make accurate predictions.
Motivated by our success in a recent pathology competition (Camelyon Grand Challenge 2016), we are applying deep learning techniques to massive aggregated sets of pathology data to build algorithms to automatically detect and diagnose medical conditions – with the goal of helping hundreds of millions of people receive fast, accurate diagnosis.
Co-Founder & CEO
Andy earned his MD from Brown Medical School and completed residency and fellowship training in Anatomic Pathology and Molecular Genetic Pathology from Stanford University. He completed a PhD in Biomedical Informatics from Stanford University, where he developed one of the first machine-learning based systems for cancer pathology. He is board certified by the American Board of Pathology in Anatomic Pathology and Molecular Genetic Pathology. He joined the faculty of Harvard Medical School in 2011, where he is now an Associate Professor (Part-time). He has published over 85 publications in the fields of cancer biology, cancer pathology, and biomedical informatics.
Co-Founder & CTO
Aditya is currently completing his PhD in machine learning and computer vision at MIT. He completed his MS at Stanford in 2011 and BS at Caltech in 2009. In his research, he has developed new methods for an array of applications in computer vision, including eye-tracking, prediction of image memorability, and visualization of deep networks. He is the recepient of a Facebook Fellowship and his work has been widely covered by various media outlets including BBC, The New York Times and The Washington Post. He has published over 30 papers in the fields of deep learning, computer vision and neuroscience.