Ordun, CatherineCha, AlexandraRaff, EdwardPurushotham, SanjayKwok, KarenRule, MasonGulley, James2023-09-132023-09-132023-08-23https://doi.org/10.48550/arXiv.2308.12271http://hdl.handle.net/11603/296592nd Annual Artificial Intelligence over Infrared Images for Medical Applications Workshop (AIIIMA) at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023)Since thermal imagery offers a unique modality to investigate pain, the U.S. National Institutes of Health (NIH) has collected a large and diverse set of cancer patient facial thermograms for AI-based pain research. However, differing angles from camera capture between thermal and visible sensors has led to misalignment between Visible-Thermal (VT) images. We modernize the classic computer vision task of image registration by applying and modifying a generative alignment algorithm to register VT cancer faces, without the need for a reference or alignment parameters. By registering VT faces, we demonstrate that the quality of thermal images produced in the generative AI downstream task of Visible-to-Thermal (V2T) image translation significantly improves up to 52.5\%, than without registration. Images in this paper have been approved by the NIH NCI for public dissemination.10 pagesen-USThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.Public Domain Mark 1.0http://creativecommons.org/publicdomain/mark/1.0/Text