Talk Summary 

Histopathological images are the gold standard tool for cancer diagnosis, whose interpretation requires manual inspection by expert pathologists. This process is time-consuming for the patients and subject to human error. Recent advances in deep learning models, particularly convolutional neural networks, combined with big databases of patient histopathology images will pave the path for cancer researchers to create more accurate guiding tools for pathologists. 

In this talk, I will review the latest advances of big data in healthcare analytics and focus on deep learning applications in cancer research. Targeted at a general audience, I will provide a high-level overview of technical concepts in deep learning image analysis, and describe a typical cloud-based workflow for tackling such big data problems. I will conclude my talk by sharing some of our most recent results based on a wide range of cancer types.

Biography

Mohammad Soltanieh-ha is a Clinical Assistant Professor at Information Systems Department at Boston University. He obtained his Ph.D. in computational condensed matter physics from Northeastern University. Shortly after, Mohammad joined Infor Dynamic Science Labs as a data scientist where he worked on building AI applications and applied a variety of machine learning and optimization algorithms on a range of big data problems in healthcare inventory systems, finance, pricing optimization, and retail. Currently, he teaches data analytics at BU and his research revolves around applications of deep learning in automating histopathological diagnosis.

A light lunch will be served on a first-come, first-served basis.