Multi-scale feature fusion for image-level kidney stone classification on NCCT images: a cross-center generalization evaluation.
- 1The School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 10083, China.
- 2Department of Urology, Shenzhen Bao'an Shiyan People's Hospital, Shenzhen, Guangdong, 518000, China. [email protected].
Abstract
Kidney stones are a common and serious urinary tract disease. Due to their small size, indistinct boundaries, and differences in imaging protocols and scanners across centers, deep learning-based image-level classification on non-contrast computed tomography (NCCT) images still faces challenges in reliably distinguishing stone-positive images from normal images and achieving robust cross-center performance. To this end, we developed a deep learning-based image-level NCCT kidney stone classification framework that encompasses dataset construction, unified preprocessing, systematic comparison of image enhancement strategies, modeling of a multi-scale feature-fusion convolutional neural network (MSF-CNN), and cross-center generalization evaluation. Within this framework, MSF-CNN enhances the representation of stone-related features by fusing multi-scale and global contextual information and introducing a channel attention mechanism. We evaluated 1,496 coronal NCCT images from 497 subjects at a single center with institutional ethics approval, and additionally used 369 images from a publicly available external dataset as an independent external test set. Seven image enhancement methods were systematically compared, and ablation experiments were conducted under a unified preprocessing pipeline. The results showed that Laplacian sharpening achieved the best performance among the evaluated enhancement methods, yielding 99.16% accuracy and 99.97% AUC on the internal test set and 97.02% accuracy and 98.50% AUC on the external test set when combined with MSF-CNN. Overall, the proposed framework achieved highly accurate and cross-center-robust image-level classification of stone-positive and normal NCCT images, providing a potential auxiliary screening tool for clinical decision support.
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BibTeX
@article{gao2026multiscale,
title = {{Multi-scale feature fusion for image-level kidney stone classification on NCCT images: a cross-center generalization evaluation.}},
author = {Hanlin Gao and Hongyao Chen and Huan Deng and Weiping Wang and Chao Yao and Chenjing Guo},
journal = {BMC nephrology},
year = {2026},
month = jul,
doi = {10.1186/s12882-026-05228-x},
url = {https://europepmc.org/article/MED/42958077},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Multi-scale feature fusion for image-level kidney stone classification on NCCT images: a cross-center generalization evaluation.
AU - Hanlin Gao
AU - Hongyao Chen
AU - Huan Deng
AU - Weiping Wang
AU - Chao Yao
AU - Chenjing Guo
PY - 2026
DA - 2026/07/31
JO - BMC nephrology
DO - 10.1186/s12882-026-05228-x
UR - https://europepmc.org/article/MED/42958077
AB - Kidney stones are a common and serious urinary tract disease. Due to their small size, indistinct boundaries, and differences in imaging protocols and scanners across centers, deep learning-based image-level classification on non-contrast computed tomography (NCCT) images still faces challenges in reliably distinguishing stone-positive images from normal images and achieving robust cross-center performance. To this end, we developed a deep learning-based image-level NCCT kidney stone classification framework that encompasses dataset construction, unified preprocessing, systematic comparison of image enhancement strategies, modeling of a multi-scale feature-fusion convolutional neural network (MSF-CNN), and cross-center generalization evaluation. Within this framework, MSF-CNN enhances the representation of stone-related features by fusing multi-scale and global contextual information and introducing a channel attention mechanism. We evaluated 1,496 coronal NCCT images from 497 subjects at a single center with institutional ethics approval, and additionally used 369 images from a publicly available external dataset as an independent external test set. Seven image enhancement methods were systematically compared, and ablation experiments were conducted under a unified preprocessing pipeline. The results showed that Laplacian sharpening achieved the best performance among the evaluated enhancement methods, yielding 99.16% accuracy and 99.97% AUC on the internal test set and 97.02% accuracy and 98.50% AUC on the external test set when combined with MSF-CNN. Overall, the proposed framework achieved highly accurate and cross-center-robust image-level classification of stone-positive and normal NCCT images, providing a potential auxiliary screening tool for clinical decision support.
SN - 1471-2369
ER -