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For: Bouteldja N, Klinkhammer BM, Bülow RD, Droste P, Otten SW, Freifrau von Stillfried S, Moellmann J, Sheehan SM, Korstanje R, Menzel S, Bankhead P, Mietsch M, Drummer C, Lehrke M, Kramann R, Floege J, Boor P, Merhof D. Deep Learning-Based Segmentation and Quantification in Experimental Kidney Histopathology. J Am Soc Nephrol 2021;32:52-68. [PMID: 33154175 DOI: 10.1681/ASN.2020050597] [Cited by in Crossref: 7] [Cited by in F6Publishing: 1] [Article Influence: 3.5] [Reference Citation Analysis]
Number Citing Articles
1 Arthurs C, Roufosse C. Forging the tools for a computer-aided workflow in transplant pathology. Lancet Digit Health 2022;4:e2-3. [PMID: 34794931 DOI: 10.1016/S2589-7500(21)00254-5] [Reference Citation Analysis]
2 Kers J, Bülow RD, Klinkhammer BM, Breimer GE, Fontana F, Abiola AA, Hofstraat R, Corthals GL, Peters-Sengers H, Djudjaj S, von Stillfried S, Hölscher DL, Pieters TT, van Zuilen AD, Bemelman FJ, Nurmohamed AS, Naesens M, Roelofs JJTH, Florquin S, Floege J, Nguyen TQ, Kather JN, Boor P. Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study. Lancet Digit Health 2022;4:e18-26. [PMID: 34794930 DOI: 10.1016/S2589-7500(21)00211-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Chen X, Huang H, Heidari AA, Sun C, Lv Y, Gui W, Liang G, Gu Z, Chen H, Li C, Chen P. An efficient multilevel thresholding image segmentation method based on the slime mould algorithm with bee foraging mechanism: A real case with lupus nephritis images. Computers in Biology and Medicine 2022;142:105179. [DOI: 10.1016/j.compbiomed.2021.105179] [Reference Citation Analysis]
4 Klinkhammer BM, Buchtler S, Djudjaj S, Bouteldja N, Palsson R, Edvardsson VO, Thorsteinsdottir M, Floege J, Mack M, Boor P. Current kidney function parameters overestimate kidney tissue repair in reversible experimental kidney disease. Kidney Int 2022:S0085-2538(22)00337-4. [PMID: 35483527 DOI: 10.1016/j.kint.2022.02.039] [Reference Citation Analysis]
5 Kim H, Yoon H, Thakur N, Hwang G, Lee EJ, Kim C, Chong Y. Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain. Sci Rep 2021;11:22520. [PMID: 34795365 DOI: 10.1038/s41598-021-01905-z] [Reference Citation Analysis]
6 Hodgin JB, Mariani LH, Zee J, Liu Q, Smith AR, Eddy S, Hartman J, Hamidi H, Gaut JP, Palmer MB, Nast CC, Chang A, Hewitt S, Gillespie BW, Kretzler M, Holzman LB, Barisoni L; Nephrotic Syndrome Study Network (NEPTUNE). Quantification of Glomerular Structural Lesions: Associations With Clinical Outcomes and Transcriptomic Profiles in Nephrotic Syndrome. Am J Kidney Dis 2021:S0272-6386(21)01004-0. [PMID: 34864148 DOI: 10.1053/j.ajkd.2021.10.004] [Reference Citation Analysis]
7 Girolami I, Pantanowitz L, Marletta S, Hermsen M, van der Laak J, Munari E, Furian L, Vistoli F, Zaza G, Cardillo M, Gesualdo L, Gambaro G, Eccher A. Artificial intelligence applications for pre-implantation kidney biopsy pathology practice: a systematic review. J Nephrol 2022. [PMID: 35441256 DOI: 10.1007/s40620-022-01327-8] [Reference Citation Analysis]
8 Shubham S, Jain N, Gupta V, Mohan S, Ariffin MM, Ahmadian A. Identify glomeruli in human kidney tissue images using a deep learning approach. Soft Comput. [DOI: 10.1007/s00500-021-06143-z] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
9 Huo Y, Deng R, Liu Q, Fogo AB, Yang H. AI applications in renal pathology. Kidney Int 2021;99:1309-20. [PMID: 33581198 DOI: 10.1016/j.kint.2021.01.015] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
10 Luo Y, Liang J, Hu X, Tang Z, Zhang J, Han L, Dong Z, Deng W, Miao B, Ren Y, Na N. Deep Learning Algorithms for the Prediction of Posttransplant Renal Function in Deceased-Donor Kidney Recipients: A Preliminary Study Based on Pretransplant Biopsy. Front Med 2022;8:676461. [DOI: 10.3389/fmed.2021.676461] [Reference Citation Analysis]