Normal kidney images help detect rare glomerulus abnormalities accurately
Seeing Abnormal from Normal: Glomerular Abnormality in Representations of Normal Renal Morphology
Computer Vision and Pattern Recognition
Summary
Detecting kidney problems in tiny parts called glomeruli is tricky because some abnormalities are rare or missing in training data. The authors use a model trained only on normal kidney images to spot unusual ones by measuring how different they look from normal patterns. Their technique, called NoRDeC, analyzes features at different model layers to find and understand abnormalities without needing examples of each problem type. Tests showed it performs better than some existing methods in identifying various glomerular abnormalities. This approach helps detect kidney disease signs even when rare abnormal cases are not available for training.
What this means in practice
- •For medical image analysts: Automatically screen kidney biopsy images to find unusual glomeruli without needing examples of all possible diseases.
- •For clinical pathology labs: Support pathologists by flagging potential kidney glomerulus abnormalities for further expert review using a model trained only on healthy samples.
Authors
Greta Hasko, Rachit Saluja, Tianyu Shi, Leiyue Zhao, Yuechen Yang, Daniel Reisenbuechler, Tianyuan Yao, Zhenhao Guo, John Cannon, Haichun Yang, Yuankai Huo, Yuling Chi, Lorraine Gudas, Mert R. Sabuncu, Yihe Yang, Ruining Deng
Abstract
Fine-grained evaluation of glomerular pathology must distinguish normal glomeruli from abnormalities such as global and segmental glomerulosclerosis, obsolescent, ischemic, solidified, disappearing, and atubular glomeruli. Supervised classification requires labeled examples of every category, which is impractical when subtypes are rare or absent from the training cohort. One-class anomaly detection offers an alternative by modeling normal data and scoring deviations, allowing previously unseen abnormalities to be detected. We use the frozen residual U-Net backbone of Omni-Seg, pretrained to segment structurally normal renal primitives without abnormal-subtype labels. We propose NoRDeC (Normal-Reference Detection and Characterization), a framework combining Mahalanobis normal-reference scoring with layer-wise representation analysis to determine whether and where glomerular pathology is encoded, how spatial aggregation affects detection, and whether abnormalities alter inter-layer relationships differently. Using glomerular images from two institutions, we evaluate backbone layers and aggregation strategies, compare NoRDeC with PaDiM and PatchCore, and analyze representations using centered kernel alignment (CKA). Layer 4 with Center-70 aggregation achieved a pooled AUROC of $0.926\pm0.013$. NoRDeC achieved the highest AUROC in six of seven abnormality categories and in the pooled analysis, while CKA suggested subtype-dependent changes in inter-layer relationships not captured by anomaly scores alone. The normal-reference model is fitted using only normal glomeruli; abnormality labels are used for configuration selection, evaluation, and grouping in the representation analysis. These results show that a frozen renal feature extractor can support both detection and representation-level characterization of glomerular abnormalities without using abnormal examples to fit the detector.