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Cited by in F6Publishing
For: Swiderska-Chadaj Z, de Bel T, Blanchet L, Baidoshvili A, Vossen D, van der Laak J, Litjens G. Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer. Sci Rep 2020;10:14398. [PMID: 32873856 DOI: 10.1038/s41598-020-71420-0] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Mehrvar S, Himmel LE, Babburi P, Goldberg AL, Guffroy M, Janardhan K, Krempley AL, Bawa B. Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives. J Pathol Inform 2021;12:42. [PMID: 34881097 DOI: 10.4103/jpi.jpi_36_21] [Reference Citation Analysis]
2 Salvi M, Acharya UR, Molinari F, Meiburger KM. The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis. Computers in Biology and Medicine 2021;128:104129. [DOI: 10.1016/j.compbiomed.2020.104129] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 11.0] [Reference Citation Analysis]
3 Ayyad SM, Shehata M, Shalaby A, Abou El-Ghar M, Ghazal M, El-Melegy M, Abdel-Hamid NB, Labib LM, Ali HA, El-Baz A. Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey. Sensors (Basel) 2021;21:2586. [PMID: 33917035 DOI: 10.3390/s21082586] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Jang HJ, Lee A, Kang J, Song IH, Lee SH. Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach. World J Gastroenterol 2021; 27(44): 7687-7704 [PMID: 34908807 DOI: 10.3748/wjg.v27.i44.7687] [Reference Citation Analysis]
5 Shao Y, Nir G, Fazli L, Goldenberg L, Gleave M, Black P, Wang J, Salcudean S. Improving prostate cancer classification in H&E tissue micro arrays using Ki67 and P63 histopathology. Comput Biol Med 2020;127:104053. [PMID: 33126125 DOI: 10.1016/j.compbiomed.2020.104053] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
6 Runz M, Rusche D, Schmidt S, Weihrauch MR, Hesser J, Weis CA. Normalization of HE-stained histological images using cycle consistent generative adversarial networks. Diagn Pathol 2021;16:71. [PMID: 34362386 DOI: 10.1186/s13000-021-01126-y] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
7 Thagaard J, Stovgaard ES, Vognsen LG, Hauberg S, Dahl A, Ebstrup T, Doré J, Vincentz RE, Jepsen RK, Roslind A, Kümler I, Nielsen D, Balslev E. Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers. Cancers (Basel) 2021;13:3050. [PMID: 34207414 DOI: 10.3390/cancers13123050] [Reference Citation Analysis]