Colormaps Matter: Evaluating Their Impact on CNN-Based Clinical Thermal Imaging

Thermal imaging is an increasingly used modality for clinical deep learning, yet a key preprocessing step in convolutional neural network (CNN)-based pipelines has gone largely unexamined. Each thermal image encodes scalar temperature values rendered into RGB via a colormap, determining how temperature information is visually represented. While existing work shows colormap selection influences human interpretation of thermal images, its impact on CNN-based classification remains unstudied. We address this gap through an evaluation of five colormaps across three pretrained CNN architectures and two clinical prediction tasks. Our results show that colormap choice produces consistent differences in predictive performance and substantially shifts the spatial regions CNNs attend to when making predictions. These findings suggest that colormap selection must be treated and reported as a factor shaping model behavior, with direct implications for how CNN-based pipelines are developed, validated, and deployed in clinical thermal imaging.