StoneXAI-Net: an explainable weakly supervised framework for kidney stone classification, localization, and segmentation from CT images

Introduction Kidney stone disease (urolithiasis) is one of the most frequent urological diseases worldwide; it is highly recurrent and carries a significant clinical burden and economic cost. Non-contrast computed tomography (CT) is the primary diagnostic modality and offers the highest sensitivity and specificity. However, automated kidney stone analysis remains challenging because expert pixel-level annotations are scarce, hyperdense non-stone anatomy causes mislocalization in weakly supervised localization, and explainable artificial intelligence (XAI) methods are often used circularly, both for weakly supervised localization and for its subsequent interpretation. Methods We present StoneXAI-Net, an explainable, weakly supervised framework to classify, localize, and segment kidney stones from CT images. The framework consists of three modules: (i) a CT-physics augmentation strategy that diversifies training and mitigates overfitting; (ii) consensus pseudo-labeling that combines complementary explanation maps with uncertainty maps and anatomical priors to suppress false localizations; and (iii) a faithfulness audit module with deletion, insertion, and pointing-game metrics that objectively evaluates explanation quality. The resulting consensus pseudo-labels were used to train an attention-prior-conditioned Attention U-Net for segmentation. The framework was evaluated on 3,364 axial abdominal CT images (1,577 stone and 1,787 normal) using five-fold cross-validation. Results StoneXAI-Net achieved 97.4% accuracy, 98.4% precision, 96.0% recall, a 97.1% F1-score, and a 99.5% AUC. Against consensus pseudo-masks, the full segmentation configuration achieved a proxy Dice coefficient of 0.834, an IoU of 0.777, and an HD95 of 2.94 px; anatomical post-processing reduced HD95 by 6.26 px. Discussion StoneXAI-Net is a feasible and interpretable framework for the automated analysis of kidney stones that substantially reduces the time-consuming and costly manual annotation of kidney stones by experts. Because segmentation was evaluated against pseudo-masks at the slice level, these results are proxy estimates that require confirmation with expert annotations and patient-level validation.