Plenary Event
SPIE Medical Imaging Awards and Plenary
19 February 2023 • 5:30 PM - 6:30 PM PST | Town & Country A
5:30 pm:
Symposium Chair Welcome and Robert F. Wagner All-Conference Best Student Paper Award Announcement
Robert M. Nishikawa, University of Pittsburgh (United States) and Despina Kontos, Univ. of Pennsylvania (United States) welcome all SPIE Medical Imaging 2022 attendees and will announce the first-place winner and runner-up of the Robert F. Wagner All-Conference Best Student Paper Award.
Award Sponsored by:
5:40 pm:
Acknowledgment of New SPIE Fellows
Recognition of members of the medical imaging community who have been selected this year as new SPIE Fellows.
5:45 pm:
SPIE Harrison H. Barrett Award in Medical Imaging
Presented in recognition of outstanding accomplishments in medical imaging.
5:50 pm:
Reliable deep learning in dynamic environments
Zachary Chase Lipton, Carnegie Mellon Univ. (United States)
Claims of deep learning’s superhuman performance on medical tasks are typically predicated on evaluations in which the training data and test data sets are exchangeably (statistically identical), differentiated only by the random partitioning of a larger population from which both are sampled. However, this assumption is always violated in practice, sometimes with disastrous effects. Subtle differences in how images are captured or represented can bring an otherwise superhuman system down to unacceptably poor levels of accuracy. In this talk I will discuss the fundamental hardness of these problems—absent further assumptions on the nature of the shifts in distribution, no principled technique can exist for estimating the accuracy of models, let alone for adapting them to new environments. I will also discuss the vast landscape of (i) principled methods (and corresponding assumptions) for developing robust and adaptive predictive models and (ii) heuristic methods that have shown some promise on benchmarks but lack theory to guide their application.
Zachary Chase Lipton is an Assistant Professor of Machine Learning and Operations Research at Carnegie Mellon University and a Visiting Scientist at Amazon AI. He directs the Approximately Correct Machine Intelligence (ACMI) lab, whose research focuses including the theoretical and engineering foundations of robust and adaptive machine-learning algorithms, applications to both prediction and decision-making problems in clinical medicine, natural language processing, and the impact of machine-learning systems on society. A key theme in his current work is to take advantage of causal structure underlying the observed data while dealing with the messy high-dimensional data that typifies deep-learning settings. He is the founder of the Approximately Correct blog (approximatelycorrect.com) and a co-author of Dive into Deep Learning, an interactive open-source book drafted entirely through Jupyter notebooks. He can be found on Twitter (@zacharylipton), GitHub (@zackchase), or his lab's website (acmilab.org).
Symposium Chair Welcome and Robert F. Wagner All-Conference Best Student Paper Award Announcement
Robert M. Nishikawa, University of Pittsburgh (United States) and Despina Kontos, Univ. of Pennsylvania (United States) welcome all SPIE Medical Imaging 2022 attendees and will announce the first-place winner and runner-up of the Robert F. Wagner All-Conference Best Student Paper Award.
Award Sponsored by:
5:40 pm:
Acknowledgment of New SPIE Fellows
Recognition of members of the medical imaging community who have been selected this year as new SPIE Fellows.
5:45 pm:
SPIE Harrison H. Barrett Award in Medical Imaging
Presented in recognition of outstanding accomplishments in medical imaging.
5:50 pm:
Reliable deep learning in dynamic environments
Zachary Chase Lipton, Carnegie Mellon Univ. (United States)
Claims of deep learning’s superhuman performance on medical tasks are typically predicated on evaluations in which the training data and test data sets are exchangeably (statistically identical), differentiated only by the random partitioning of a larger population from which both are sampled. However, this assumption is always violated in practice, sometimes with disastrous effects. Subtle differences in how images are captured or represented can bring an otherwise superhuman system down to unacceptably poor levels of accuracy. In this talk I will discuss the fundamental hardness of these problems—absent further assumptions on the nature of the shifts in distribution, no principled technique can exist for estimating the accuracy of models, let alone for adapting them to new environments. I will also discuss the vast landscape of (i) principled methods (and corresponding assumptions) for developing robust and adaptive predictive models and (ii) heuristic methods that have shown some promise on benchmarks but lack theory to guide their application.
Zachary Chase Lipton is an Assistant Professor of Machine Learning and Operations Research at Carnegie Mellon University and a Visiting Scientist at Amazon AI. He directs the Approximately Correct Machine Intelligence (ACMI) lab, whose research focuses including the theoretical and engineering foundations of robust and adaptive machine-learning algorithms, applications to both prediction and decision-making problems in clinical medicine, natural language processing, and the impact of machine-learning systems on society. A key theme in his current work is to take advantage of causal structure underlying the observed data while dealing with the messy high-dimensional data that typifies deep-learning settings. He is the founder of the Approximately Correct blog (approximatelycorrect.com) and a co-author of Dive into Deep Learning, an interactive open-source book drafted entirely through Jupyter notebooks. He can be found on Twitter (@zacharylipton), GitHub (@zackchase), or his lab's website (acmilab.org).