Machine Learning in Medicine (MLxMed) Seminar: Ender Konukoglu, Associate Professor, ETH Zurich
Machine Learning in Medicine (MLxMed)
A Virtual Seminar Series in Pittsburgh
Hosted by the Department of Biomedical Informatics, University of Pittsburgh; Boston University Hariri Institute for Computing; and the University of Toronto
Date: Friday, January 26, 2024
Time: 2:00 PM – 3:00 PM Eastern Time
Location: Zoom https://pitt.zoom.us/j/91009318707
(Details are listed at the end)
Speaker: Ender Konukoglu, PhD, Associate Professor, ETH Zurich
Talk Title: “Expert load matters: operating networks at high accuracy and low manual effort”
Abstract: Integration of advanced machine learning algorithms in critical applications require the algorithms to be robust enough. Such systems should not lead to mistakes with severe consequences and advance what humans can achieve without the algorithm. One avenue towards this end is to consider Human-AI collaboration systems, where learning algorithms aim to reduce work load of a human expert. Algorithms do most of the work but cases that are challenging for the algorithm are planned to be sent to the human expert, such that the collaboration leads to both efficient and accurate workflow. In this talk, I will present our recent work which takes a step in this direction. I will discuss how accuracy and human load can both be integrated in a loss function and present results using neural networks trained with the proposed loss on multiple data sets. Across all the experiments, we observed that more accurate networks that require fewer delegations to human experts could be achieved. I will conclude with the current limitations and directions forward.
About MLxMed Seminar Series
Medicine is complex and data-driven, while discovery and decision-making are increasingly enabled by machine learning. Machine learning has the potential to support, enable and improve medical discovery and clinical decision making in areas such as medical imaging, cancer diagnostics, precision medicine, clinical trials, and electronic health records. This seminar series focuses on new algorithms, real-world deployment, and future trends in machine learning in medicine. It will feature prominent investigators who are developing and applying machine learning to biomedical discovery and in clinical decision support. For more information, see MLxMed website.
Boston University Faculty Host: Kayhan Batmanghelich, Assistant Professor (ECE)
Zoom Information
When: Friday, January 26, 2024, 2:00 PM Eastern Time (US and Canada)
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Genine M. Bartolotta
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Department of Biomedical Informatics
University of Pittsburgh, School of Medicine
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