MLxMED: Machine Learning in Medicine Seminar

  • Remote Access - Zoom
  • Virtual Presentation
  • ULAS BAGCI
  • Associate Professor, Radiology and Biomedical Engineering Department\,
  • Northwestern University, and
  • Professor, Center for Research in Computer Vision, Department of Computer Science, University of Central Florida
Seminars

A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning

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 the visual search processes of radiologists. However, most relevant studies in this aspect are not compatible with realistic radiology reading rooms. In this talk, I will share our unique experience for developing a paradigm-shifting computer-aided diagnosis (CAD) system, called collaborative CAD (C-CAD), that unifies CAD and eye-tracking systems in realistic radiology room settings. In other words, we are creating artificial intelligence (AI) tools that get benefits from human cognition and improve over complementary powers of AI and human intelligence. We first developed an eye-tracking interface providing radiologists with a real radiology reading room experience. Second, we proposed a novel computer algorithm that unifies eye-tracking data and a CAD system. The proposed C-CAD collaborates with radiologists via eye-tracking technology and helps them to improve their diagnostic decisions. The proposed C-CAD system has been tested in a lung and prostate cancer screening experiment with multiple radiologists. More recently, we also experimented with brain tumor segmentation with the proposed technology leading to promising results. In the last part of my talk, I will describe how to develop AI algorithms that are trusted by clinicians, namely “explainable AI algorithms". By embedding explainability into black-box nature of deep learning algorithms, it will be possible to deploy AI tools into clinical workflow and leading into more intelligent and less artificial algorithms available in radiology rooms.   

Hosted by the Department of Biomedical Informatics, University of Pittsburgh

For more information see MLxMed website.

Zoom Participation. See announcement.

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