Machine and Hybrid Intelligence
Machine and Hybrid Intelligence Lab aims to accelerate research on machine learning field to develop solutions for high-risk artificial intelligence (AI) problems such as applications that utilize biomedical imaging. Specifically, Dr. Bagci and his lab have been focusing on two sub-fields of AI:
▫ Explainable/interpretable deep learning and
▫ Human-centered AI
for creating trustable solutions for biomedical imaging applications such as cancer detection, diagnosis, and risk assessment.
Projects
Computer Aided Diagnosis (CAD)
Computer aided diagnosis (CAD) tools help radiologists to reduce diagnostic errors such as missing tumors and misdiagnosis. Vision researchers have been analyzing behaviors of radiologists during screening to understand how and why they miss tumors or misdiagnose. In this regard, eye-trackers have been instrumental in understanding visual search processes of radiologists. However, most relevant studies in this aspect are not compatible with realistic radiology reading rooms. In this study, we aim to develop a paradigm shifting CAD system, called collaborative CAD (C-CAD), that unifies CAD and eye-tracking systems in realistic radiology room settings.