Digital Diagnostic Modeling using Bioimaging and Machine Learning

This will be an online seminar.

Seminar Speaker: Shachi Mittal, University of Washington

Abstract: Cancer and many other diseases are diagnosed by histopathology, a process that involves biopsy and tissue staining followed by a manual examination by a pathologist. This approach is susceptible to misdiagnosis, low concordance rates across pathology labs, and long-turnaround times. In this talk, I will describe Mittal Research Lab’s work on using multispectral imaging for molecular profiling, recognizing the disease, and characterizing its microenvironment. I will discuss the use of deep learning directly on stained imaging data (clinical ground truth) for a rapid and reliable diagnosis. I will also illustrate the use of deep learning approaches for a quantitative understanding of the molecularly stained images and digital risk stratification models for the early detection of disease. 

Bio: Shachi Mittal is an Assistant Professor in the Department of Chemical Engineering at the University of Washington and an Adjunct Assistant Professor in the Department of Laboratory Medicine and Pathology. She received her Ph.D. from the Department of Bioengineering at UIUC in 2019. Shachi earned her B.S. and M.S in Biochemical Engineering from the Indian Institute of Technology in Delhi in 2014. She has acquired training and expertise in spectral imaging, cancer biology, pathology, and artificial intelligence. She is passionate about interfacing engineering technologies with clinical science to develop systems useful for patient care, particularly for cancer management. She has received several awards and fellowships, such as the Baxter Young Investigator award, William G. Fateley award, Tomas B. Hirschfeld Scholar award, Illinois Distinguished Fellowship, and Big Data Fellowship for computational medicine.