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Glomerulus morphology on renal pathology images provides valuable diagnosis and outcome prediction information. To provide better care, an efficient, standardized, and scalable method is urgently needed to optimize the time-consuming and labor-intensive interpretation process by renal pathologists. This paper proposes a deep convolutional neural network (CNN)-based approach to automatically detect and classify glomeruli with different stains in renal pathology images. In the glomerulus detection stage, this paper proposes a flattened Xception with a feature pyramid network (FX-FPN). The FX-FPN is employed as a backbone in the framework of faster region-based CNN to improve glomerulus detection performance. In the classification stage, this paper considers classifications of five glomerulus morphologies using a flattened Xception classifier. To endow the classifier with higher discriminability, this paper proposes a generative data augmentation approach for patch-based glomerulus morphology augmentation. New glomerulus patches of different morphologies are generated for data augmentation through the cycle-consistent generative adversarial network (CycleGAN). The single detection model shows the F 1 score up to 0.9524 in H&E and PAS stains. The classification result shows that the average sensitivity and specificity are 0.7077 and 0.9316, respectively, by using the flattened Xception with the original training data. The sensitivity and specificity increase to 0.7623 and 0.9443, respectively, by using the generative data augmentation. Comparisons with different deep CNN models show the effectiveness and superiority of the proposed approach. • Collection of renal pathological images with annotations of glomerulus classes. • A flattened Xception with feature pyramid network (FX-FPN) for glomerulus detection. • Generative glomerulus morphology augmentation to improve classification performance. • A two-stage detection and classification framework with performance evaluation. [ABSTRACT FROM AUTHOR]

[Display omitted] • LIPSS upon titanium through femtosecond laser structuring. • LIPSS upon titanium own the significant chemical and surface characteristics. • LIPSS upon titanium efficiently enhance photoelectrochemical dopamine sensing. • Polarization-shaped femtosecond laser affect the photoelectrochemical sensing. Femtosecond laser structuring in the fabrication of periodic nanostructure on titanium for enhanced photoelectrochemical dopamine sensing performance was reported. The formation of laser induced periodic surface structures (LIPSS) upon titanium foil depended on the polarization of the femtosecond laser beam. When linearly and radially polarized femtosecond laser beam interacted with titanium foil, LIPSS were generated on titanium surface and further used for the fabrication of electrode material in photoelectrochemical dopamine sensing. Field emission scanning electron microscopy (FESEM), X-ray photoelectron spectroscopy (XPS), and UV–vis absorption spectra were performed to study the surface characteristics of the laser treated titanium foil. The results revealed that low spatial frequency LIPSS (LSFL) formation occurred at both cases of the linearly and radially polarized laser treated titanium foil with a period of about 350 and 250 nm and owned the significant surface chemical and structural modification (abundant Ti4+ state and hydroxyl functional group), especially in term of radially polarized laser treated titanium foil. The radially polarized laser treated titanium foil owning optimal surface structural modification contributed multiple catalytically active sites and made the outstanding photoelectrochemical performance of dopamine sensing. It was concluded that femtosecond laser surface structuring made it possible to modify the surface characteristics of objects for future construction of sensing platforms. [ABSTRACT FROM AUTHOR]