Pre-Cancer AutoHist: A Software Application to Classify Lung Pre-Cancer Histologic Features

FALL 2019 RESEARCH INCUBATION AWARDEES

PI: Jennifer Beane, Assistant Professor of Medicine, MED
Track: Digital Health Initiative

Lung cancer is the leading cause of cancer death, and the pre-cancer lesion is an important clinical indicator of lung cancer risk to help therapies intercept the cancer development process in the early stage to increase survival. In this case, the team proposes a software application to accurately and automatically identify histologic features of lung pre-cancerous lesions based on digitized whole-slide images. The Human in the Loop (HITL) machine will be used to automate the analyze and generate user-friendly web interface-based application (App).