Task-Aware Color Constancy for Chronic Kidney Disease Prediction

Smartphone-based kidney health prediction offers a low-cost alternative to laboratory testing, but its accuracy degrades significantly under unconstrained lighting conditions that distort the colorimetric signals of creatinine test strips. Standard color constancy methods, which optimize for perceptual visual quality, often fail to preserve the precise spectral cues required for clinical quantification. To address this, we propose a Task-Aware Color Constancy (TACC) framework that explicitly aligns illumination correction with downstream disease prediction. Our approach integrates a conditional generative adversarial network (TACC-GAN) with NeurSPR, a neural structured prediction module. Crucially, we introduce a differentiable histogram layer based on Kernel Density Estimation (KDE), which bridges the “non-differentiable bottleneck” of traditional regressors. This allows diagnostic losses to backpropagate directly to the generator, optimizing color correction for clinical accuracy rather than visual appeal. We evaluate on a demographically stratified dataset constructed from US population statistics. Among deployable (reference-free) methods, TACC achieves the best kidney-health classification performance in every lighting condition, raising the F1-score from 0.50 to 0.87 under extreme lighting variations while keeping creatinine-estimation error competitive with the strongest baseline. Notably, it approaches oracle calibration methods that require a white-light reference at test time, despite using no such reference, opening the door towards reliable point-of-care monitoring under real-world settings.