Cited by 344AI in cancer detectionMedical Image Segmentation TechniquesRadiomics and Machine Learning in Medical Imaging

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Deep Learning-Based Histopathologic Assessment of Kidney Tissue

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Hermsen, Meyke, de Bel, Thomas, den Boer, Marjolijn, Steenbergen, Eric J., Kers, Jesper, Florquin, Sandrine, Roelofs, Joris J. T. H., Stegall, Mark D., Alexander, Mariam P., Smith, Byron H., Smeets, Bart, Hilbrands, Luuk B., van der Laak, Jeroen A. W. M.·2019·Journal of the American Society of Nephrology

DOI 10.1681/asn.2019020144PMC PMC6779356

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Abstract

Significance Statement Histopathologic assessment of kidney tissue currently relies on manual scoring or traditional image-processing techniques to quantify and classify tissue features, time-consuming approaches that have limited reproducibility. The authors present an alternative approach, featuring a convolutional neural network for multiclass segmentation of kidney tissue in sections stained by periodic acid–Schiff. Their findings demonstrate applicability of convolutional neural networks for tissue from multiple centers, for biopsies and nephrectomy samples, and for the analysis of both healthy and pathologic tissues. In addition, they validated the network’s results with components from the Banff classification system. Their convolutional neural network may have utility for quantitative studies involving kidney histopathology across centers and potential for application in routine diagnostics. Background The development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolutional neural network for multiclass segmentation of digitized kidney tissue sections stained with periodic acid–Schiff (PAS). Methods We trained the network using multiclass annotations from 40 whole-slide images of stained kidney transplant biopsies and applied it to four independent data sets. We assessed multiclass segmentation performance by calculating Dice coefficients for ten tissue classes on ten transplant biopsies from the Radboud University Medical Center in Nijmegen, The Netherlands, and on ten transplant biopsies from an external center for validation. We also fully segmented 15 nephrectomy samples and calculated the network’s glomerular detection rates and compared network-based measures with visually scored histologic components (Banff classification) in 82 kidney transplant biopsies. Results The weighted mean Dice coefficients of all classes were 0.80 and 0.84 in ten kidney transplant biopsies from the Radboud center and the external center, respectively. The best segmented class was “glomeruli” in both data sets (Dice coefficients, 0.95 and 0.94, respectively), followed by “tubuli combined” and “interstitium.” The network detected 92.7% of all glomeruli in nephrectomy samples, with 10.4% false positives. In whole transplant biopsies, the mean intraclass correlation coefficient for glomerular counting performed by pathologists versus the network was 0.94. We found significant correlations between visually scored histologic components and network-based measures. Conclusions This study presents the first convolutional neural network for multiclass segmentation of PAS-stained nephrectomy samples and transplant biopsies. Our network may have utility for quantitative studies involving kidney histopathology across centers and provide opportunities for deep learning applications in routine diagnostics.

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Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
The Banff 97 working classification of renal allograft pathology
Racusen, L. C., Solez, K., Colvin, R. B., Bonsib, S. M., de Castro, M. C. R., Cavallo, T., Croker, B. P., Demetris, A. J., Drachenberg, C. B., Fogo, A. B., et al. (1999) · Kidney International · doi:10.1046/j.1523-1755.1999.00299.x · Cited by 3,026
Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen - van de Kaa, C., Bult, P., van Ginneken, B., & van der Laak, J. (2016) · Scientific Reports · doi:10.1038/srep26286 · Cited by 1,069
Self-citation
A 2018 Reference Guide to the Banff Classification of Renal Allograft Pathology
Roufosse, C., Simmonds, N., Clahsen-van Groningen, M., Haas, M., Henriksen, K. J., Horsfield, C., Loupy, A., Mengel, M., Perkowska-Ptasińska, A., Rabant, M., et al. (2018) · Transplantation · doi:10.1097/tp.0000000000002366 · Cited by 751
The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology
Loupy, A., Haas, M., Solez, K., Racusen, L. C., Glotz, D., Serón, D., Nankivell, B. J., Colvin, R. B., Afrouzian, M., Akalin, E., et al. (2016) · American Journal of Transplantation · doi:10.1111/ajt.14107 · Cited by 629
The Substantial Loss of Nephrons in Healthy Human Kidneys with Aging
Đenić, A., Lieske, J. C., Chakkera, H. A., Poggio, E. D., Alexander, M. P., Singh, P. Y., Kremers, W. K., Lerman, L. O., & Rule, A. D. (2016) · Journal of the American Society of Nephrology · doi:10.1681/asn.2016020154 · Cited by 453
Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
Tellez, D., Balkenhol, M., Otte-Höller, I., van de Loo, R., Vogels, R., Bult, P., Wauters, C., Vreuls, W., Mol, S., Karssemeijer, N., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2820199 · Cited by 288
Computerized Image Analysis of Sirius Red–Stained Renal Allograft Biopsies as a Surrogate Marker to Predict Long-Term Allograft Function
Grimm, P. C., Nickerson, P. W., Gough, J., McKenna, R. M., Stern, E., Jeffery, J., & Rush, D. N. (2003) · Journal of the American Society of Nephrology · doi:10.1097/01.asn.0000066143.02832.5e · Cited by 193

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Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration
Fujii, Y., Uchida, D., Sato, R., Obata, T., Matsumi, A., Miyamoto, K., Morimoto, K., Terasawa, H., Yamazaki, T., Matsumoto, K., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-72312-3 · Cited by 21
“Recently, computer-aided diagnosis (CAD) using AI models has been developed in various clinical fields to support the shortage of pathologists 23,24 .”
Method
Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions
Cazzaniga, G., Rossi, M., Eccher, A., Girolami, I., L’Imperio, V., Nguyen, H. V., Becker, J. U., Bueno, G., Sbaraglia, M., Tos, A. P. D., et al. (2023) · Journal of Nephrology · doi:10.1007/s40620-023-01775-w · Cited by 21
“Finally, a multiclass segmentation model made its first appearance in 2019 as a single-stain model and was later followed by a multi-stain one [31, 52].”
Method
Automatic Evaluation of Histological Prognostic Factors Using Two Consecutive Convolutional Neural Networks on Kidney Samples
Marechal, E., Jaugey, A., Tarris, G., Paindavoine, M., Seibel, J., Martin, L., de la Vega, M. F., Crepin, T., Ducloux, D., Zanetta, G., et al. (2021) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.07830621 · Cited by 20
“detected 93% and 76%, respectively (16).”
BackgroundMethodResultInfluential
Lung allograft standardized histological analysis (LASHA) template: A research consensus proposal
Calabrese, F., Roden, A. C., Pavlisko, E. N., Lunardi, F., Neil, D., Adam, B., Hwang, D. M., Goddard, M. J., Berry, G. J., Ivanovic, M., et al. (2022) · The Journal of Heart and Lung Transplantation · doi:10.1016/j.healun.2022.06.021 · Cited by 20
Evaluating tubulointerstitial compartments in renal biopsy specimens using a deep learning-based approach for classifying normal and abnormal tubules
Hara, S., Haneda, E., Kawakami, M., Morita, K., Nishioka, R., Zoshima, T., Kometani, M., Yoneda, T., Kawano, M., Karashima, S., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271161 · Cited by 20
“achieved multiclass segmentation through U-Net, which showed high DCs on multiclass structures, using whole-slide images obtained from multicenter institutions [4].”
BackgroundMethodInfluential
Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments
Farris, A. B., Alexander, M. P., Balis, U. J., Barisoni, L. M. C., Boor, P., Bülow, R. D., Cornell, L. D., Demetris, A. J., Farkash, E. A., Hermsen, M., et al. (2023) · Transplant International · doi:10.3389/ti.2023.11783 · Cited by 21
“This project builds upon former research by Hermsen et al. [33, 34] that developed AI (more speci ﬁ cally: DL) for automated assessment of histopathologic features in digitized kidney tissue sections.”
Natural Language Processing in Diagnostic Texts from Nephropathology
Legnar, M., Daumke, P., Hesser, J., Porubský, Š., Popovic, Z., Bindzus, J. N., Siemoneit, J.-H. H., & Weis, C. (2022) · Diagnostics · doi:10.3390/diagnostics12071726 · Cited by 20
“For instance, there were several works from the field of image analysis or respectively computational pathology published by many different groups in recent years [5,6].”
Background
Prognostic Implications of a Morphometric Evaluation for Chronic Changes on All Diagnostic Native Kidney Biopsies
Đenić, A., Bogojevic, M., Mullan, A. F., Sabov, M., Asghar, M. S., Sethi, S. M., Smith, M. L., Fervenza, F. C., Glassock, R. J., Hommos, M. S., et al. (2022) · Journal of the American Society of Nephrology · doi:10.1681/asn.2022030234 · Cited by 19
A preliminary study on the application of deep learning methods based on convolutional network to the pathological diagnosis of PJI
Tao, Y., Hu, H. M., Li, J., Li, M., Zheng, Q., Zhang, G., & Ni, M. (2022) · Arthroplasty · doi:10.1186/s42836-022-00145-4 · Cited by 19
“trained a convolutional neural network [22] for multi-class segmentation of digital renal tissue sections and it was able to identify glomeruli, renal tubules and interstitia in digitalized renal tissue sections.”
Background
Glomerulus Detection Using Segmentation Neural Networks
Samant, S. S., Chauhan, A., Jagadish, D. N., & Singh, V. K. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00764-y · Cited by 20
“The authors of [29] utilized CNNs and created a network from an ensemble of five UNets for segmentation of ten tissue classes from WSIs of periodic acid-Schiff (PAS)–stained kidney transplant biopsies.”
Method
Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine
Lucarelli, N., Ginley, B. G., Zee, J., Mimar, S., Paul, A. S., Jain, S. R., Han, S. S., Rodrigues, L., Ozrazgat‐Baslanti, T., Wong, M. L., et al. (2023) · Kidney360 · doi:10.34067/kid.0000000000000299 · Cited by 19
Artificial intelligence-enhanced interpretation of kidney transplant biopsy: focus on rejection
Farris, A. B., van der Laak, J. A. W. M., & van Midden, D. (2025) · Current Opinion in Organ Transplantation · doi:10.1097/mot.0000000000001213 · Cited by 18
Advances in computational nephropathology
Hölscher, D. L., Bülow, R. D., Strauch, M., & Boor, P. (2025) · Kidney International · doi:10.1016/j.kint.2025.06.029 · Cited by 19
Glomerulus Semantic Segmentation Using Ensemble of Deep Learning Models
Gu, Y., Ruan, R., Yan, Y., Zhao, J. G., Sheng, W., Liang, L., & Huang, B. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-022-06608-9 · Cited by 17
“Unet [10], 2019 10 classes segmentation, F-score: 84%”
PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation
Deng, R., Liu, Q., Cui, C., Yao, T., Yue, J., Xiong, J., Yu, L., Wu, Y., Yin, M., Wang, Y., et al. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01115 · Cited by 18
“…using deep learning methods [4, 12, 35, 44, 51], they still encounter three major limitations: (1) Current multi-network and multi-head designs [11, 25, 27, 37, 48, 52] focus only on single tissue structures or structures at similar scales, lacking a comprehensive approach to achieve…”
Background
Constrained Multi-scale Dense Connections for Accurate Biomedical Image Segmentation
Zhang, J., Zhang, Y., Zhu, S., & Xu, X. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313254 · Cited by 18
“A CCURATE segmentation of biomedical issues such as the morphology of histological structures including colon [5], esophagi [6], and kidney [7] is an essential pre-requisite to obtain reliable morphological statistics, which is widely used for quantitative diagnosis.”
The Role of Machine Learning in Cardiovascular Pathology
Glass, C., Lafata, K. J., Jeck, W. R., Horstmeyer, R. W., Cooke, C., Everitt, J. I., Glass, M., Dov, D., & Seidman, M. A. (2021) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.11.008 · Cited by 18
Identification of lymph node metastasis in pre‐operation cervical cancer patients by weakly supervised deep learning from histopathological whole‐slide biopsy images
Liu, Q., Jiang, N., Hao, Y., Hao, C., Wang, W., Bian, T., Wang, X., Li, H., Zhang, Y., Kang, Y., et al. (2023) · Cancer Medicine · doi:10.1002/cam4.6437 · Cited by 17
“41,42 It is currently handled by specialized experts, pathologists who carefully assess gigapixel images.”
Background
Application of spatial-omics to the classification of kidney biopsy samples in transplantation
Tasca, P., van den Berg, B. M., Rabelink, T. J., Wang, G., Heijs, B., van Kooten, C., de Vries, A. P. J., & Kers, J. (2024) · Nature Reviews Nephrology · doi:10.1038/s41581-024-00861-x · Cited by 17
Machine Learning Comes to Nephrology
Lemley, K. V. (2019) · Journal of the American Society of Nephrology · doi:10.1681/asn.2019070664 · Cited by 17
Map3D: Registration-Based Multi-Object Tracking on 3D Serial Whole Slide Images
Deng, R., Yang, H., Jha, A., Lu, Y., Chu, P., Fogo, A. B., & Huo, Y. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3069154 · Cited by 16
Glo-In-One: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining
Yao, T., Lu, Y., Long, J., Jha, A., Zhu, Z., Asad, Z., Yang, H., Fogo, A. B., & Huo, Y. (2022) · Journal of medical imaging · doi:10.1117/1.jmi.9.5.052408 · Cited by 18
“An initial step for renal tissue assessment is the differentiation and segmentation of relevant tissue structures in kidney specimens as it is performing pixel-level quantification on the image.(32,60,61) However, segmentation on WSIs is challenging since the pixel-level annotation is computational extensive on the gi…”
Deep Learning for Image Analysis in Kidney Care
Zhang, H., Botler, M., & Kooman, J. P. (2022) · Advances in Kidney Disease and Health · doi:10.1053/j.akdh.2022.11.003 · Cited by 16
Segmentation of human functional tissue units in support of a Human Reference Atlas
Jain, Y., Godwin, L. L., Ju, Y., Sood, N., Quardokus, E. M., Bueckle, A., Longacre, T. A., Horning, A. M., Lin, Y., Esplin, E. D., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04848-5 · Cited by 21
“7 and 8.”
Method
Automation and Decision Support in the Area of Nephrology Using Numerical Algorithms, Artificial Intelligence, and Expert Approach: Review of the Current State of Knowledge
Pawuś, D., Porażko, T., & Paszkiel, S. (2024) · IEEE Access · doi:10.1109/access.2024.3413595 · Cited by 21
“The articles [64], [65], [66], [67], [68], [69] collectively delve into the integration of advanced computational techniques, particularly machine learning and deep learning models, in the domain of renal medicine.”
Background
Towards accurate and efficient diagnoses in nephropathology: An AI-based approach for assessing kidney transplant rejection
Fayzullin, A. L., Иванова, Е., Grinin, V., Ermilov, D., Solovyeva, S. E., Balyasin, M. V., Bakulina, A., Nikitin, P. V., Valieva, Y., Kalinichenko, A. M., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.08.011 · Cited by 16
“Automated evaluation of tissue vascularization and immune cell infiltration was used to assess inflammation in parenchymal and stromal parts of kidney [8,10 – 12].”
BackgroundMethodInfluential
Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma
Roetzer-Pejrimovsky, T., Nenning, K., Kiesel, B., Klughammer, J., Rajchl, M., Baumann, B., Langs, G., & Wöehrer, A. (2024) · GigaScience · doi:10.1093/gigascience/giae057 · Cited by 16
“In digital pathology, major ac hie v ements included the precise segmentation of individual cells [ 4–7 ], histologic structures [ 8 , 9 ], and tumor tissues [ 10 ].”
Deep Learning Algorithms for the Prediction of Posttransplant Renal Function in Deceased-Donor Kidney Recipients: A Preliminary Study Based on Pretransplant Biopsy
Luo, Y., Liang, J., Hu, X., Tang, Z., Zhang, J., Han, L., Dong, Z., Deng, W., Miao, B., Ren, Y., et al. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2021.676461 · Cited by 16
“(18) presented a CNN for multiclass segmentation of PAS-stained kidney samples, with which the glomeruli, tubules, and interstitium were well-classified.”
Background
Bifidobacterium bifidum alleviates adenine-induced acute kidney injury in mice by improving intestinal barrier function
Yang, M., Zhao, M., Ma, Q., Hua, Q., Hu, J., Zhou, Q., Yi, H., Zhang, Z., & Zhang, L. (2024) · Food & Function · doi:10.1039/d4fo02014f · Cited by 15
Constrained multi-scale dense connections for biomedical image segmentation
Zhang, J., Zhang, Y., Qiu, H., Wang, T., Li, X., Zhu, S., Huang, M., Zhuang, J., Shi, Y., & Xu, X. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111031 · Cited by 15
Precision transplant pathology
Wood, M. A., Xu, Q., Zeevi, A., Randhawa, P. S., Lesniak, D., & Demetris, A. J. (2020) · Current Opinion in Organ Transplantation · doi:10.1097/mot.0000000000000772 · Cited by 15
“glomeruli) and analyze tissue specimens [38,39].”
Building robust pathology image analyses with uncertainty quantification
Gomes, J., Kong, J., Kurç, T., de Melo, A. C. M. A., Ferreira, R. C., Saltz, J. H., & Teodoro, G. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106291 · Cited by 16
Classification of renal biopsy direct immunofluorescence image using multiple attention convolutional neural network
Zhang, L., Li, M., Wu, Y., Fang, H., Wang, C., Han, W., Niu, D., & Zheng, W. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106532 · Cited by 15
Interobserver variability between experienced and inexperienced observers in the histopathological analysis of Wilms tumors: a pilot study for future algorithmic approach
Rutgers, J. J., Bánki, T., van der Kamp, A., Waterlander, T. J., Vermeulen, M. A., van den Heuvel‐Eibrink, M. M., van der Laak, J. A. W. M., Fiocco, M. F., Mavinkurve‐Groothuis, A. M. C., & de Krijger, R. R. (2021) · Diagnostic Pathology · doi:10.1186/s13000-021-01136-w · Cited by 14
“Therefore, this study can serve as the basis for further development of automated component analysis of pre-treated WT by an ML-based algorithm, which may lead to more accurate and reproducible risk group classification of WT [7].”
BackgroundMethodInfluential
Coherent anti-Stokes Raman scattering cell imaging and segmentation with unsupervised data analysis
Boildieu, D., Ben, T. G.-D., Duponchel, L., Sol, V., Petit, J., Champion, É., Kano, H., Helbert, D., Magnaudeix, A., Leproux, P., et al. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.933897 · Cited by 16
“The accuracy of the segmentation included in the MC analysis was evaluated by calculating the Dice similarity coefficients that are well recognized as a valuable metrics (Hermsen et al., 2019; Eelbode et al., 2020).”
Method
Automated Deep Learning-Based Classification of Wilms Tumor Histopathology
van der Kamp, A., de Bel, T., van Alst, L., Rutgers, J. J., van den Heuvel‐Eibrink, M. M., Mavinkurve‐Groothuis, A. M. C., van der Laak, J. A. W. M., & de Krijger, R. R. (2023) · Cancers · doi:10.3390/cancers15092656 · Cited by 14
“[21] developed a U-net for multiclass segmentation of kidney tissue with a Dice score ranging from 0.”
Method
Automated evaluation with deep learning of total interstitial inflammation and peritubular capillaritis on kidney biopsies
Jacq, A., Tarris, G., Jaugey, A., Paindavoine, M., Maréchal, E., Bard, P., Rebibou, J.-M., Ansart, M., Calmo, D., Bamoulid, J., et al. (2023) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfad094 · Cited by 14
The Dawn of a New Era in Kidney Transplantation: Promises and Limitations of Artificial Intelligence for Precision Diagnostics
Peloso, A., Naesens, M., & Thaunat, O. (2023) · Transplant International · doi:10.3389/ti.2023.12010 · Cited by 14
“In 2019, Hermsen et al. [8] proposed the ﬁ rst convolutional neural network (cNN) applied to kidney biopsies.”
Method
MSMTSeg: Multi-Stained Multi-Tissue Segmentation of Kidney Histology Images via Generative Self-Supervised Meta-Learning Framework
Liu, X., Wang, R., Lai, Y., Wu, Y., Cheng, H., Lu, Y., Zhang, J., Hao, N., Ban, C., Wang, Y., et al. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3381047 · Cited by 14
“For the segmentation of whole kidney tissue, Hermsen et al. train a network using multi-tissue annotations from PAS-stained kidney transplant biopsy WSIs [17].”
Artificial intelligence assists identification and pathologic classification of glomerular lesions in patients with diabetic nephropathy
Lei, Q., Hou, X., Liu, X., Liang, D., Fan, Y., Xu, F., Liang, S., Liang, D., Yang, J., Xie, G., et al. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05221-8 · Cited by 15
“In the field of renal pathology, deep learning algorithms have been applied successfully to identify glomeruli, tubules, and interstitium in digital slides from transplant biopsies [17] or patients with Minimal Change Disease [18].”
Method
Deep learning-based histopathological assessment of tubulo-interstitial injury in chronic kidney diseases
Suzuki, N., Kojima, K., Malvica, S., Yamasaki, K., Chikamatsu, Y., Oe, Y., Nagasawa, T., Kondo, E., Sanada, S., Aiba, S., et al. (2025) · Communications Medicine · doi:10.1038/s43856-024-00708-3 · Cited by 15
“While previous studies have focused on developing deep learning-based methods for accurate segmentation of the glomerulus 27 – 29 and tubulo-interstitium 30,31 , many did not extend their research to include downstream analysis of segmentation results.”
Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence
Patel, A., Shaker, N., Mohanty, S. K., Sharma, S., Gangal, S., Eloy, C., & Parwani, A. V. (2022) · Diagnostics · doi:10.3390/diagnostics12081778 · Cited by 15
“…identiﬁcation and segmentation of glomeruli on WSI of human kidney biopsies demonstrated accurate discrimination of non-glomerular images from glomerular images that were either normal or partially sclerosed (NPS) or globally sclerosed (GS) (Accuracy: 92.67% ± 2.02%, Kappa: 0.8681 ± 0.0392) [60].”
BackgroundInfluential
Classification of glomerular pathological findings using deep learning and nephrologist–AI collective intelligence approachPreprint
Uchino, E., Suzuki, K., Sato, N., Kojima, R., Tamada, Y., Hiragi, S., Yokoi, H., Yugami, N., Minamiguchi, S., Haga, H., et al. (2020) · medRxiv · doi:10.1101/2019.12.30.19016162 · Cited by 14
“While some studies have validated DL models analyzing the structures other than the glomeruli, such as the tubules, blood vessels, and interstitium [7–10], many studies have focused on the glomeruli, which present various histological findings essential for diagnosis.”
Background
Automated Segmentation of Kidney Nephron Structures by Deep Learning Models on Label-free Autofluorescence Microscopy for Spatial Multi-omics Data Acquisition and MiningPreprint
Patterson, N. H., Pingry, E. L., Moser, F. A., Kruse, A. R. S., Dufresne, M., Djambazova, K., Esselman, A. B., Brewer, M., Ardenne, J. M. V., Allen, J. L., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.16.452703 · Cited by 12
Evaluation of Vision Transformers for Multi-Organ Tumor Classification Using MRI and CT Imaging
Martín, Ó. A., & Sánchez, J. (2025) · Electronics · doi:10.3390/electronics14152976 · Cited by 13
Application of Multi-Scale Fusion Attention U-Net to Segment the Thyroid Gland on Localized Computed Tomography Images for Radiotherapy
Wen, X., Zhao, B., Yuan, M., Li, J., Sun, M., Ma, L., Sun, C., & Yang, Y. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.844052 · Cited by 13
“org 5 DSC (25) and JSC (26) were calculated according to formulas (1.”
Method
Digital Pathology: A Comprehensive Review of Open-Source Histological Segmentation Software
Pavone, A. M., Giannone, A. G., Cabibi, D., D’Aprile, S., Denaro, S., Salvaggio, G., Parenti, R., Yezzi, A. J., & Comelli, A. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4010012 · Cited by 13
“Owing to the progression of digital pathology, several detection and classiﬁcation tools have been suggested, such as for brain and breast tumor classiﬁcation, and for the histopathological assessment of kidney tissue [46,47].”
Recent Advances in Endoscopic Ultrasound for Gallbladder Disease Diagnosis
Takahashi, K., Ozawa, E., Shimakura, A., Mori, T., Miyaaki, H., & Nakao, K. (2024) · Diagnostics · doi:10.3390/diagnostics14040374 · Cited by 14
Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy
Kawazoe, Y., Shimamoto, K., Yamaguchi, R., Nakamura, I., Yoneda, K., Shinohara, E., Shintani‐Domoto, Y., Ushiku, T., Tsukamoto, T., & Ohe, K. (2022) · Diagnostics · doi:10.3390/diagnostics12122955 · Cited by 15
“Previous studies [18,19,22] have assessed the usefulness of segmentation results in subsequent analyses, whereas other studies have only assessed the performance of machine learning models.”
BackgroundMethodResultInfluential
Evaluation of pediatric hydronephrosis using deep learning quantification of fluid-to-kidney-area ratio by ultrasonography
Lin, Y., Khong, P., Zou, Z., & Cao, P. (2021) · Abdominal Radiology · doi:10.1007/s00261-021-03201-w · Cited by 13
Deep Learning-Based Model Significantly Improves Diagnostic Performance for Assessing Renal Histopathology in Lupus Glomerulonephritis
Shen, L., Sun, W., Zhang, Q., Wei, M., Xu, H., Luo, X., Wang, G., & Zhou, F. (2022) · Kidney Diseases · doi:10.1159/000524880 · Cited by 12
“To diminish manual quantitative evaluation, deep learning (also known as deep structured learning) technology has been applied to digital whole-slide image (WSI) segmentation [4–6].”
Method
Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis
Feng, C., Ong, K., Young, D. M., Chen, B., Li, L., Huo, X., Lu, H., Gu, W., Liu, F., Tang, H., et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad740 · Cited by 12
“…to various kidney structures like tubules, capillaries, and arterioles, covering pathologies such as interstitial fibrosis and tubular atrophy (Kolachalama et al. 2018, Mariani et al. 2018, Hermsen et al. 2019, Uchino et al. 2020, Bu¨low et al. 2021, Ginley et al. 2021, Jayapandian et al. 2021).”
Background
Morphomics via next-generation electron microscopy
Son, R., Yamazawa, K., Oguchi, A., Suga, M., Tamura, M., Yanagita, M., Murakawa, Y., & Kume, S. (2023) · Journal of Molecular Cell Biology · doi:10.1093/jmcb/mjad081 · Cited by 13
“…assessments, the relationship between renal histology and the prognosis/severity of renal diseases has been examined us-ing image recognition and comprehensive segmentation of the constituent tissues of renal samples, including human kidney biopsies (Hermsen et al., 2019 ; Bouteldja et al., 2021 ).”
A graph-learning based model for automatic diagnosis of Sjögren’s syndrome on digital pathological images: a multicentre cohort study
Wu, R., Chen, Z., Yu, J., Lai, P., Chen, X., Han, A., Xu, M. Q., Fan, Z., Cheng, B., Jiang, Y. L., et al. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05550-8 · Cited by 13
“…research reports have attested to the high performance and precision of deep learning algorithms in pathology image diagnostics, with diagnostic outcomes paralleling or surpassing human performance, encompassing ailments afflicting organs, for example, the kidneys, liver, brain, and lungs [19–22].”
Background
Discovery and generalization of tissue structures from spatial omics data
Wu, Z., Kondo, A., McGrady, M., Baker, E. A. G., Chidester, B., Wu, E., Rahim, M. K., Bracey, N. A., Charu, V., Cho, R. J., et al. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100838 · Cited by 13
Perspectives in systems nephrology
Lindenmeyer, M. T., Alakwaa, F., Rose, M. P., & Kretzler, M. (2021) · Cell and Tissue Research · doi:10.1007/s00441-021-03470-3 · Cited by 12
Identification of glomerulosclerosis using IBM Watson and shallow neural networks
Pesce, F., Albanese, F., Mallardi, D., Rossini, M., Pasculli, G., Suavo-Bulzis, P., Granata, A. R. M., Brunetti, A., Cascarano, G. D., Bevilacqua, V., et al. (2022) · Journal of Nephrology · doi:10.1007/s40620-021-01200-0 · Cited by 12
“…[6–9], and Machine Learning (ML) algorithms have already been successfully applied to glomerular segmentation by different research groups [7, 10–13] comprehending a whole-slide classifier to directly replicate a pathologist’s assessment of IFTA and glomeru-losclerosis on renal biopsy…”
Method
Automated detection algorithm for C4d immunostaining showed comparable diagnostic performance to pathologists in renal allograft biopsy
Choi, G., Kim, Y.-G., Cho, H., Kim, N., Lee, H., Moon, K. C., & Go, H. (2020) · Modern Pathology · doi:10.1038/s41379-020-0529-9 · Cited by 12
Multistain segmentation of renal histology: first steps toward artificial intelligence–augmented digital nephropathology
Bülow, R. D., Kers, J., & Boor, P. (2020) · Kidney International · doi:10.1016/j.kint.2020.08.025 · Cited by 12
Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment
Yuan, S., Guo, L., & Xu, F. (2025) · International Urology and Nephrology · doi:10.1007/s11255-025-04878-4 · Cited by 14
Artificial intelligence: is there a potential role in nephropathology?
Hermsen, M., Smeets, B., Hilbrands, L. B., & van der Laak, J. A. W. M. (2020) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfaa181 · Cited by 11
Interstitial fibrosis and tubular atrophy measurement via hierarchical extractions of kidney and atrophy regions with deep learning method
Lai, Y., Liu, X., Wu, Y., Zhou, D., Wang, C., Niu, D., Han, W., Zhou, X., Chen, J., & Zheng, W. (2022) · Measurement · doi:10.1016/j.measurement.2022.111885 · Cited by 11
Classification of Diabetic Rat Histopathology Images Using Convolutional Neural Networks
Yurttakal, A., Erbay, H., Çinarer, G., & Baş, H. (2020) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.d.201110.001 · Cited by 7
Multi-Task Learning-Based Immunofluorescence Classification of Kidney Disease
Pan, S., Fu, Y., Chen, P., Liu, J., Liu, W., Wang, X., Cai, G., Yin, Z., Wu, J., Tang, L., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182010798 · Cited by 11
“AI has been widely used in the recognition of renal pathology [29], such as the recognition of cortical or medulla, glomeruli, renal tubules, and renal arteries [30,31], as well as the recognition of internal glomerular structures, such as the podocytes, mesangial cells, and mesangial area [32].”
Background
Recent Advances and Future Perspectives in the Use of Machine Learning and Mathematical Models in Nephrology
Galuzio, P. P., & Cherif, A. (2022) · Advances in Chronic Kidney Disease · doi:10.1053/j.ackd.2022.07.002 · Cited by 12
Deep learning-based glomerulus detection and classification with generative morphology augmentation in renal pathology images
Juang, C., Chuang, Y., Lin, G., Chung, I., & Lo, Y.-C. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102375 · Cited by 10
End-to-end interstitial fibrosis assessment of kidney biopsies with a machine learning-based model
Liu, Z., Lin, C., Wang, H., Wen, M., Lin, W., Huang, S., Tu, K., Kuo, C., & Chen, T. (2022) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfac143 · Cited by 11
Whole slide imaging-based deep learning to predict the treatment response of patients with non-small cell lung cancer
Pan, Y., Sheng, W., Shi, L., Jing, D., Jiang, W., Chen, J., Wang, H., & Qiu, J. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-22-1098 · Cited by 9
The current state of artificial intelligence in cardiac transplantation
GOSWAMI, R. M. (2021) · Current Opinion in Organ Transplantation · doi:10.1097/mot.0000000000000875 · Cited by 10
“Multiple centers around the globe have utilized this technology within renal allograft rejection biopsy samples [19]; however, none have been able to develop models to accurately predict factors that discrepancy in predictive factors versus anticipation is ultimately because of the current lack of…”
Background
Systematic reporting of medical kidney biopsies
Leh, S. M., & Dendooven, A. (2021) · Clinical Kidney Journal · doi:10.1093/ckj/sfab140 · Cited by 10
Automated scoring of total inflammation in renal allograft biopsies
Smith, B. H., Grande, J. P., Ryan, M., Smith, M. L., Đenić, A., Hermsen, M., Park, W. D., Kremers, W. K., & Stegall, M. D. (2022) · Clinical Transplantation · doi:10.1111/ctr.14837 · Cited by 10
Unsupervised stain augmentation enhanced glomerular instance segmentation on pathology images
Yang, F., He, Q., Wang, Y., Zeng, S., Xu, Y., Ye, J., He, Y., Guan, T., Wang, Z., & Li, J. (2024) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-024-03154-7 · Cited by 9
“Hermsen et al. [27] applied the U-Net framework to the nephrectomy and transplantation biopsy samples and achieved high segmentation accuracy for glomeruli in PAS-stained kidney biopsy sections.”
Vision transformer introduces a new vitality to the classification of renal pathology
Zhang, J., Lu, J. D., Chen, B., Pan, S., Jin, L., Zheng, Y., & Pan, M. (2024) · BMC Nephrology · doi:10.1186/s12882-024-03800-x · Cited by 9
“For example, Bel and Hermsen et al. developed and compared two fully convolutional networks, assessing the performance of UNet for classification tasks [12, 14, 15].”
Background
Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody–Associated Glomerulonephritis
Trejo, M. A. C. W., Sadeghi, M., Singh, S., Mahmoodian, N., Sharifli, S., Hrušková, Z., Tesař, V., Puéchal, X., BRUIJN, J. A., Göbel, G., et al. (2024) · Kidney International Reports · doi:10.1016/j.ekir.2024.11.005 · Cited by 9
Using Image Registration and Machine Learning to Develop a Workstation Tool for Rapid Analysis of Glomeruli in Medical Renal Biopsies
Wilbur, D. C., Pettus, J. R., Smith, M. L., Cornell, L. D., Andryushkin, A., Wingard, R., & Wirch, E. (2020) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_49_20 · Cited by 10
“[21] In the present study, the extension of this prior work to a practical application of all 4 stains in a human renal biopsy makes this a prototypical example of results which might be expected in a real‑world setting.”
Background
EasierPath: An Open-Source Tool for Human-in-the-Loop Deep Learning of Renal Pathology
Zhu, Z., Lu, Y., Deng, R., Yang, H., Fogo, A. B., & Huo, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61166-8_23 · Cited by 9
KDClassifier: A urinary proteomic spectra analysis tool based on machine learning for the classification of kidney diseases
Zhao, W., Zhang, Y., Li, X., Mao, Y., Wu, C., Zhao, L., Liu, F., Zhu, J., Cheng, J., Yang, H., et al. (2021) · Aging Pathobiology and Therapeutics · doi:10.31491/apt.2021.09.064 · Cited by 9
“In the future, an artificial intelligence-assisted pathological analysis tool will expand the understanding of renal pathological lesions and the pathogenesis of kidney disease [7-9].”
Method
Telemedicine‐medical “snack community”‐PHS ecosystem: Insights into the double‐edged sword role of telemedicine in clinical practice and medical education during the COVID‐19 pandemic and beyond
Feng, D., Shi, X., Wang, J., Zhang, L., Xiao, Y., Li, D., Wu, R., Wei, W., Miyamoto, A., Yoo, K. H., et al. (2024) · Exploration · doi:10.1002/exp.20230111 · Cited by 8
HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis
Deng, R., Liu, Q., Cui, C., Yao, T., Xiong, J., Bao, S., Li, H., Yin, M., Wang, Y., Zhao, S., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72083-3_15 · Cited by 8
Clinically applicable optimized periprosthetic joint infection diagnosis via AI based pathology
Tao, Y., Luo, Y., Hu, H. M., Wang, W., Zhao, Y., Wang, S., Zheng, Q., Zhang, T., Zhang, G., Li, J., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01301-7 · Cited by 10
“While neural networks excel at identifying tumors and speci ﬁ c tissues 37 , recognizing infected tissues remains challenging due to the dispersed and varied nature of cells in infection.”
Background
A Spatially Guided Machine-Learning Method to Classify and Quantify Glomerular Patterns of Injury in Histology Images
Besusparis, J., Morkūnas, M., & Laurinavičius, A. (2023) · Journal of Imaging · doi:10.3390/jimaging9100220 · Cited by 8
“Recent progress in digital image analysis and machine-learning applications has opened new prospects for automated renal pathology assays for both segmentation and quantiﬁcation tasks [16–35].”
Background
Exploring the Potential of Claude 3 Opus in Renal Pathological Diagnosis: Performance Evaluation
Li, X., Liu, K., Lang, Y., Chai, Z., & Liu, F. (2024) · JMIR Medical Informatics · doi:10.2196/65033 · Cited by 7
Kidney Disease Detection and Identification Using Artificial Intelligence
Saraswat, T., Pathak, S., Sachdeva, S., Sahu, K., & Sawhney, R. (2023) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence56041.2023.10048830 · Cited by 8
“CNN helps in undertaking the challenge of finding patterns that are present in different sort of image dataset [33].”
Background
Deep learning-based multi-model approach on electron microscopy image of renal biopsy classification
Zhang, J., & Zhang, A. (2023) · BMC Nephrology · doi:10.1186/s12882-023-03182-6 · Cited by 7
IFTA Foci Density: An Unrecognized Highly Prognostic Measurement of Fibrosis in Kidney Transplant Biopsies
Đenić, A., Rule, A. D., Park, W. D., Smith, B. H., Mejia, M. V., Kukla, A., Grande, J. P., & Stegall, M. D. (2024) · Kidney360 · doi:10.34067/kid.0000000000000514 · Cited by 7
A novel automated method for comprehensive renal cast quantification from rat kidney sections using QuPath
Yunker, L., Harwig, M. C., & Kriegel, A. J. (2024) · American Journal of Physiology-Renal Physiology · doi:10.1152/ajprenal.00252.2024 · Cited by 7
Chronic Changes on Kidney Histology by a Multiclass Artificial Intelligence Model
Đenić, A., Asghar, M. S., Stetzik, L. A., Reynolds, A., Jagtap, J. M. M., Kumar, M., Mullan, A. F., Janowczyk, A. R., Alexander, M. P., Smith, M. L., et al. (2025) · Kidney International Reports · doi:10.1016/j.ekir.2025.05.035 · Cited by 7
Large Language Models’ Clinical Decision-Making on When to Perform a Kidney Biopsy: Comparative Study
Toal, M. P., Hill, C. J., Quinn, M., O’Neill, C., & Maxwell, A. P. (2025) · Journal of Medical Internet Research · doi:10.2196/73603 · Cited by 8
“In pathology, AI has been used to rapidly characterize the nature of lesions for rapid detection and coding, allowing the pathologist to analyze specimens with greater efficiency [10].”
Artificial intelligence in genitourinary pathology
Patel, A., Parwani, A. V., & Satturwar, S. P. (2025) · Histopathology · doi:10.1111/his.70020 · Cited by 10
Classification of Glomeruli with Membranous Nephropathy on Renal Digital Pathological Images with Deep Learning
Fang, H., Li, M., Liu, X., Li, X., Yue, J., & Han, W. (2020) · Conference on Artificial Intelligence and Healthcare (CAIH) · doi:10.1145/3433996.3434486 · Cited by 8
“An improved CNN depending on 5 resembling U-net was trained on periodic acid– Schiff (PAS) stained WSIs to achieve the goal of segmentation of glomeruli, tubules and interstitium on renal pathological images [24].”
Method
Quantifying acute kidney injury in an Ischaemia-Reperfusion Injury mouse model using deep-learning-based semantic segmentation in histology
Luchian, A., Trivino-Cepeda, K., Harwood, R., Murray, P. A., Wilm, B., Kenny, S. E., Pregel, P., & Ressel, L. (2023) · Biology Open · doi:10.1242/bio.059988 · Cited by 7
“With an overall model precision of 0.85, these results align with previous work on human kidneys (Hermsen et al., 2019).”
Influential
Association of Implantation Biopsy Findings in Living Donor Kidneys With Donor and Recipient Outcomes
Emmons, B. R., Batal, I., King, K. L., Yu, M. E., Canetta, P. A., Sandoval, P. R., Mohan, S., Tsapepas, D. S., Adler, J. T., Ratner, L. E., et al. (2023) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2023.08.022 · Cited by 7
Reproducibility and prognostic ability of chronicity parameters in kidney biopsy – Comprehensive evaluation comparing microscopy and artificial intelligence in digital pathology
Ganesh, R. N., Graviss, E. A., Nguyen, D. T., El‐Zaatari, Z. M., Gaber, L. W., Barrios, R. J., Truong, L. D., & Farris, A. B. (2024) · Human Pathology · doi:10.1016/j.humpath.2024.04.006 · Cited by 7
Chronic Kidney Disease Diagnosis Using Conditional Variational Generative Adversarial Networks and Squirrel Search Algorithm
Brinda, B. M., & Rajan, C. (2023) · Information Technology And Control · doi:10.5755/j01.itc.52.4.34233 · Cited by 6
Predicting high-flow arteriovenous fistulas and cardiac outcomes in hemodialysis patients
Shah, N. A., Byrne, P., Endre, Z. H., Cochran, B. J., Barber, T. J., & Erlich, J. (2024) · Journal of Vascular Surgery · doi:10.1016/j.jvs.2024.11.028 · Cited by 6
Advances in kidney biopsy lesion assessment through dense instance segmentation
Xiong, Z., He, J., Valkema, P., Nguyen, T. Q., Naesens, M., Kers, J., & Verbeek, F. J. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103111 · Cited by 6
AI-powered insights in pediatric nephrology: current applications and future opportunities
Nada, A., Ahmed, Y., Hu, J., Weidemann, D. K., Gorman, G. H., Lecea, E. M. G., Sandokji, I. A., Cha, S., Shin, S., Bani-Hani, S., et al. (2025) · Pediatric Nephrology · doi:10.1007/s00467-025-06911-1 · Cited by 6
Transformer-based multiclass segmentation pipeline for basic kidney histology
He, J., Valkema, P., Long, J., Li, J., Florquin, S., Naesens, M., Koshy, P. J., Nguyen, T. Q., Meziyerh, S., de Vries, A. P. J., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-22814-5 · Cited by 6
“Gallego et al. 15 proposed a U-Net-based framework to quantify glomerulosclerosis in PAS and H&E stained tissues, while Ginley et al. 11 and Hermsen et al. 12 applied DL for the classification and assessment of diabetic glomerulosclerosis and other kidney pathologies.”
The promise of artificial intelligence for kidney pathophysiology
Jiang, J. J., Chan, L., & Nadkarni, G. N. (2022) · Current Opinion in Nephrology & Hypertension · doi:10.1097/mnh.0000000000000808 · Cited by 6
“As such, many studies have developed machine learning approaches to segment glomeruli derived from biopsy samples and quantify amount of sclerosis [23,34–39].”
Background
Pre-Implantation Kidney Biopsies in Extended Criteria Donors: From On Call to Expert Pathologist, from Conventional Microscope to Digital Pathology
Marletta, S., Bella, C. D., Catalano, G., Mastrosimini, M. G., Becker, J. U., Ernst, A., Rizzo, P. C., Caldonazzi, N., Vasuri, F., Malvi, D., et al. (2023) · Critical Reviews™ in Oncogenesis · doi:10.1615/critrevoncog.2023049007 · Cited by 6

Authors: Hermsen, M., de Bel, T., den Boer, M., Steenbergen, E. J., Kers, J., Florquin, S., Roelofs, J. J. T. H., Stegall, M. D., Alexander, M. P., Smith, B. H., Smeets, B., Hilbrands, L. B., & van der Laak, J. A. W. M.
Affiliations: Pathology and
Harvard University (US)
Amsterdam University Medical Centers (NL)
Mayo Clinic (US)
Radboud University Medical Center (NL)
ORCID: Hermsen, Meyke
Kers, Jesper
Florquin, Sandrine
Roelofs, Joris J. T. H.
Stegall, Mark D.
Alexander, Mariam P.
Smith, Byron H.
Smeets, Bart
Hilbrands, Luuk B.
Venue: Journal of the American Society of Nephrology
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2019-09-05
Volume: 30
Issue: 10
Pages: 1968-1979
ISSN: 1046-6673 (ISSN-L)
1533-3450
DOI: 10.1681/asn.2019020144
PMC: PMC6779356
OpenAlex: W2972214324
Cited by: 344
References: 23
Citations by year: 2026: 30 · 2025: 38 · 2024: 60 · 2023: 57 · 2022: 56 · 2021: 55 · 2020: 43 · 2019: 4
FWCI: 18.02
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Journal of the American Society of Nephrology (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Pure Amsterdam UMC (publishedVersion, other-oa)
Data Archiving and Networked Services (DANS) (submittedVersion)
Leiden Repository (Leiden University) (submittedVersion)
Amsterdam UMC (VU Amsterdam) - Institutional Repository (submittedVersion)
Radboud Repository (Radboud University) (submittedVersion)
Radboud Repository (Radboud University) (submittedVersion, other-oa)
Clinical Journal of the American Society of Nephrology
Funders: Horizon 2020 Framework Programme (9003035004)
Dutch Kidney Foundation (17OKG23)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords: kidney histopathology, convolutional neural networks, PAS staining, kidney transplant biopsy, Banff classification, whole slide images, quantitative histopathology, interstitial space
MeSH terms: Deep Learning, Biopsy, Humans, Kidney, Nephrectomy, Kidney Transplantation

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Deep Learning-Based Histopathologic Assessment of Kidney Tissue

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