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Evaluating Medical Aesthetics Treatments through Evolved Age-Estimation Models (2021)
Risto Miikkulainen
,
Elliot Meyerson
,
Xin Qiu
, Ujjayant Sinha, Raghav Kumar, Karen Hofmann, Yiyang Matt Yan, Michael Ye, Jingyuan Yang, Damon Caiazza, and Stephanie Manson Brown
Estimating a person's age from a facial image is a challenging problem with clinical applications. Several medical aesthetics treatments have been developed that alter the skin texture and other facial features, with the goal of potentially improving patient's appearance and perceived age. In this paper, this effect was evaluated using evolutionary neural networks with uncertainty estimation. First, a realistic dataset was obtained from clinical studies that makes it possible to estimate age more reliably than e.g. datasets of celebrity images. Second, a neuroevolution approach was developed that customizes the architecture, learning, and data augmentation hyperparameters and the loss function to this task. Using state-of-the-art computer vision architectures as a starting point, evolution improved their original accuracy significantly, eventually outperforming the best human optimizations in this task. Third, the reliability of the age predictions was estimated using RIO, a Gaussian-Process-based uncertainty model. Evaluation on a real-world Botox treatment dataset shows that the treatment has a quantifiable result: The patients' estimated age is reduced significantly compared to placebo treatments. The study thus shows how AI can be harnessed in a new role: To provide an objective quantitative measure of a subjective perception, in this case the proposed effectiveness of medical aesthetics treatments.
View:
PDF
Citation:
In
Proceedings of the Genetic and Evolutionary Computation Conference
, 1009–1017, 2021.
Bibtex:
@inproceedings{miikkulainen:gecco21, title={Evaluating Medical Aesthetics Treatments through Evolved Age-Estimation Models}, author={Risto Miikkulainen and Elliot Meyerson and Xin Qiu and Ujjayant Sinha and Raghav Kumar and Karen Hofmann and Yiyang Matt Yan and Michael Ye and Jingyuan Yang and Damon Caiazza and Stephanie Manson Brown}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, month={ }, pages={1009–1017}, url="http://nn.cs.utexas.edu/?miikkulainen:gecco21", year={2021} }
Presentation:
Video
People
Elliot Meyerson
Ph.D. Alumni
ekm [at] cs utexas edu
Risto Miikkulainen
Faculty
risto [at] cs utexas edu
Xin Qiu
Collaborator
xin qiu [at] cognizant com
Areas of Interest
Neuroevolution
Other Areas
Applications