Year: 2026
Type: article
Abstract: Diabetic foot ulcer (DFU) screening benefits from thermography, but infrared cameras are costly and rarely available at home. We present an unpaired cross-modal framework that virtualizes thermal imaging from ordinary smartphone photos. Using only public Kaggle data (RGB DFU photographs and a thermography foot dataset), Stage A performs RGB↔thermal translation with a CycleGAN backbone enhanced by structural-consistency constraints and anatomical priors to preserve toe, metatarsal, arch, and heel geometry. Stage B trains a thermal-domain anomaly detector (EfficientNet-B0 with a feature pyramid) and projects its responses back to RGB via a cross-domain attention back-projection module, yielding interpretable thermal risk maps. Weak labels derived from thermal asymmetry and adaptive thresholding supervise a lightweight segmentation head; uncertainty gating filters noisy pseudo-masks. In experiments (PyTorch 2.1; dual RTX 3090s), the model achieves strong cross-domain fidelity (SSIM = 0.82, LPIPS = 0.14) and clinically usable detection of thermal abnormalities (AUROC = 0.91, AUPRC = 0.88). RGB-domain localization is accurate (IoU = 0.76; Top-1 hit rate = 87%), highlighting interdigital and metatarsophalangeal regions commonly implicated in pressure-related ulceration. Physiological consistency is demonstrated by close agreement between predicted temperature rise and clinical improvement (+ 4.3 °C vs. +4.5 °C dorsal foot temperature) and by a significant association between declining model-predicted risk area and vascular recovery (ABI/TBI; r = − 0.68, p < 0.01). Ablations confirm the role of each component: removing structural loss or anatomical priors degrades geometry and clinical alignment, while removing cross-domain attention causes the largest drop (AUROC = 0.80, IoU = 0.64, correlation = 0.32), underscoring its necessity for semantic alignment and interpretability.
Source: Scientific Reports
Authors: Ping Sui, Ganchun Wu, Juan Xu, Shouguang Sui
Institutions: Shandong University, Shengli Oilfield Central Hospital
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7196927520