ArticleFrontiers in medicine2026
NephroNet: a calibration-aware, patient-disjoint benchmark for multiclass kidney CT classification with a compact depthwise-separable CNN.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: CT is the primary modality for kidney pathology, but deep-learning evaluation is undermined by slice-level leakage and poor probability calibration. Methods: A patient-disjoint, group-stratified hold-out benchmark is established for four-class kidney CT classification (Normal, Cyst, Tumor, Stone) on a 12,446-image multicenter cohort, and NephroNet - a compact (1.46 M-parameter) depthwise-separable CNN with squeeze-and-excitation and a light SpatialGate -b is proposed. A standardized pipeline (320 × 320 preprocessing; annealed MixUp/CutMix; class-weighted AdamW with warmup-cosine; EMA-only evaluation; TTA; post-hoc temperature scaling, T Results: On the hold-out ( Discussion: Transparent splits, explicit capacity control, and calibration-aware reporting support reproducible comparison; all data are single-region, so external and prospective validation is required.
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