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For: Tolkach Y, Dohmgörgen T, Toma M, Kristiansen G. High-accuracy prostate cancer pathology using deep learning. Nat Mach Intell 2020;2:411-8. [DOI: 10.1038/s42256-020-0200-7] [Cited by in Crossref: 8] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Toledo-cortés S, Useche DH, Müller H, González FA. Grading diabetic retinopathy and prostate cancer diagnostic images with deep quantum ordinal regression. Computers in Biology and Medicine 2022;145:105472. [DOI: 10.1016/j.compbiomed.2022.105472] [Reference Citation Analysis]
2 Jiao Y, Li J, Fei S. Staining condition visualization in digital histopathological whole-slide images. Multimed Tools Appl. [DOI: 10.1007/s11042-022-12559-y] [Reference Citation Analysis]
3 Gupta S, Gupta MK. A comprehensive data‐level investigation of cancer diagnosis on imbalanced data. Computational Intelligence. [DOI: 10.1111/coin.12452] [Cited by in Crossref: 7] [Cited by in F6Publishing: 2] [Article Influence: 7.0] [Reference Citation Analysis]
4 Ai K, Yuan D, Zheng J, Khalaf OI. Experimental Research on the Antitumor Effect of Human Gastric Cancer Cells Transplanted in Nude Mice Based on Deep Learning Combined with Spleen-Invigorating Chinese Medicine. Computational and Mathematical Methods in Medicine 2022;2022:1-11. [DOI: 10.1155/2022/3010901] [Reference Citation Analysis]
5 Gao Y, Ding Y, Xiao W, Yao Z, Zhou X, Sui X, Zhao Y, Zheng Y. A semi-supervised learning framework for micropapillary adenocarcinoma detection. Int J Comput Assist Radiol Surg 2022. [PMID: 35149953 DOI: 10.1007/s11548-022-02565-8] [Reference Citation Analysis]
6 Gupta S, Gupta MK, Kumar R. A Novel Multi-Neural Ensemble Approach for Cancer Diagnosis. Applied Artificial Intelligence. [DOI: 10.1080/08839514.2021.2018182] [Reference Citation Analysis]
7 Jiao Y, Yuan J, Qiang Y, Fei S. Deep embeddings and logistic regression for rapid active learning in histopathological images. Comput Methods Programs Biomed 2021;212:106464. [PMID: 34736166 DOI: 10.1016/j.cmpb.2021.106464] [Reference Citation Analysis]
8 Yang S, Linares-barranco B, Chen B. Heterogeneous Ensemble-Based Spike-Driven Few-Shot Online Learning. Front Neurosci 2022;16:850932. [DOI: 10.3389/fnins.2022.850932] [Reference Citation Analysis]
9 Jin D, Chen Y, Lu Y, Chen J, Wang P, Liu Z, Guo S, Bai X. Neutralizing the impact of atmospheric turbulence on complex scene imaging via deep learning. Nat Mach Intell 2021;3:876-84. [DOI: 10.1038/s42256-021-00392-1] [Reference Citation Analysis]
10 Marini N, Otálora S, Müller H, Atzori M. Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification. Med Image Anal 2021;73:102165. [PMID: 34303169 DOI: 10.1016/j.media.2021.102165] [Reference Citation Analysis]
11 Alarcón-zendejas AP, Scavuzzo A, Jiménez-ríos MA, Álvarez-gómez RM, Montiel-manríquez R, Castro-hernández C, Jiménez-dávila MA, Pérez-montiel D, González-barrios R, Jiménez-trejo F, Arriaga-canon C, Herrera LA. The promising role of new molecular biomarkers in prostate cancer: from coding and non-coding genes to artificial intelligence approaches. Prostate Cancer Prostatic Dis. [DOI: 10.1038/s41391-022-00537-2] [Reference Citation Analysis]