Towards Superhuman Imitation Learning for Sequential Head-and-Neck Cancer Treatment Decisions

Filippo Corna; Xinhua Zhang; Guadalupe Canahuate; Serageldin K. Attia; Abdallah Sr Mohamed; Mohamed Naser; Clifton Fuller; Elisabeta Marai

doi:10.1145/3748522.3779849

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Towards Superhuman Imitation Learning for Sequential Head-and-Neck Cancer Treatment Decisions

Conference proceeding

Open access

Towards Superhuman Imitation Learning for Sequential Head-and-Neck Cancer Treatment Decisions

Filippo Corna, Xinhua Zhang, Guadalupe Canahuate, Serageldin K. Attia, Abdallah Sr Mohamed, Mohamed Naser, Clifton Fuller and Elisabeta Marai

Proceedings of the 41st ACM/SIGAPP Symposium on Applied Computing, pp.225-226

ACM Conferences

SAC '26: 41st ACM/SIGAPP Symposium on Applied Computing

03/23/2026

DOI: 10.1145/3748522.3779849

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Abstract

This work presents the design of a simulator-driven imitation learning approach for sequential treatment decisions in head and neck cancer, built around Superhuman Policy Gradient Optimization (SPGO). Rather than simply replicating physicians' actions, the method leverages a clinical simulator to generate complete patient trajectories and incorporates an inverse-reinforcement-learning-inspired loss that rewards policies for outperforming experts on key clinical outcomes, including relapse rates and long-term toxicities.

Applied computing -- Life and medical sciences -- Health informatics

Computing methodologies -- Machine learning -- Learning paradigms -- Reinforcement learning

Computing methodologies -- Machine learning -- Learning settings -- Learning from demonstrations

Details

Title: Subtitle: Towards Superhuman Imitation Learning for Sequential Head-and-Neck Cancer Treatment Decisions
Creators: Filippo Corna - University of Illinois Chicago
Xinhua Zhang - University of Illinois Chicago
Guadalupe Canahuate - University of Iowa
Serageldin K. Attia - The University of Texas MD Anderson Cancer Center
Abdallah Sr Mohamed - The University of Texas MD Anderson Cancer Center
Mohamed Naser - The University of Texas MD Anderson Cancer Center
Clifton Fuller - The University of Texas MD Anderson Cancer Center
Elisabeta Marai - University of Illinois Chicago
Resource Type: Conference proceeding
Publication Details: Proceedings of the 41st ACM/SIGAPP Symposium on Applied Computing, pp.225-226
Conference: SAC '26: 41st ACM/SIGAPP Symposium on Applied Computing
Series: ACM Conferences
DOI: 10.1145/3748522.3779849
Publisher: ACM
Number of pages: 2
Language: English
Date published: 03/23/2026
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9985174809102771

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