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Cited by in F6Publishing
For: Xing F, Xie Y, Yang L. An Automatic Learning-Based Framework for Robust Nucleus Segmentation. IEEE Trans Med Imaging. 2016;35:550-566. [PMID: 26415167 DOI: 10.1109/tmi.2015.2481436] [Cited by in Crossref: 165] [Cited by in F6Publishing: 30] [Article Influence: 27.5] [Reference Citation Analysis]
Number Citing Articles
1 Gai H, Wang Y, Chan LLH, Chiu B. Identification of Retinal Ganglion Cells from β-III Stained Fluorescent Microscopic Images. J Digit Imaging 2020;33:1352-63. [PMID: 32705432 DOI: 10.1007/s10278-020-00365-7] [Reference Citation Analysis]
2 Xing F, Yang L. Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review. IEEE Rev Biomed Eng 2016;9:234-63. [PMID: 26742143 DOI: 10.1109/RBME.2016.2515127] [Cited by in Crossref: 224] [Cited by in F6Publishing: 46] [Article Influence: 44.8] [Reference Citation Analysis]
3 Wang B, Lei Y, Tian S, Wang T, Liu Y, Patel P, Jani AB, Mao H, Curran WJ, Liu T, Yang X. Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation. Med Phys 2019;46:1707-18. [PMID: 30702759 DOI: 10.1002/mp.13416] [Cited by in Crossref: 69] [Cited by in F6Publishing: 50] [Article Influence: 34.5] [Reference Citation Analysis]
4 Iqbal A, Sheikh A, Karayannis T. DeNeRD: high-throughput detection of neurons for brain-wide analysis with deep learning. Sci Rep 2019;9:13828. [PMID: 31554830 DOI: 10.1038/s41598-019-50137-9] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 4.5] [Reference Citation Analysis]
5 Men K, Chen X, Zhang Y, Zhang T, Dai J, Yi J, Li Y. Deep Deconvolutional Neural Network for Target Segmentation of Nasopharyngeal Cancer in Planning Computed Tomography Images. Front Oncol. 2017;7:315. [PMID: 29376025 DOI: 10.3389/fonc.2017.00315] [Cited by in Crossref: 80] [Cited by in F6Publishing: 59] [Article Influence: 20.0] [Reference Citation Analysis]
6 Cui Y, Zhang G, Liu Z, Xiong Z, Hu J. A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images. Med Biol Eng Comput 2019;57:2027-43. [PMID: 31346949 DOI: 10.1007/s11517-019-02008-8] [Cited by in Crossref: 27] [Cited by in F6Publishing: 11] [Article Influence: 13.5] [Reference Citation Analysis]
7 Khoshdeli M, Winkelmaier G, Parvin B. Deep fusion of contextual and object-based representations for delineation of multiple nuclear phenotypes. Bioinformatics 2019;35:4860-1. [PMID: 31135022 DOI: 10.1093/bioinformatics/btz430] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Zhang X, Cornish TC, Yang L, Bennett TD, Ghosh D, Xing F. Generative Adversarial Domain Adaptation for Nucleus Quantification in Images of Tissue Immunohistochemically Stained for Ki-67. JCO Clin Cancer Inform 2020;4:666-79. [PMID: 32730116 DOI: 10.1200/CCI.19.00108] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
9 Durkee MS, Abraham R, Ai J, Veselits M, Clark MR, Giger ML. Quantifying the effects of biopsy fixation and staining panel design on automatic instance segmentation of immune cells in human lupus nephritis. J Biomed Opt 2021;26. [PMID: 33420765 DOI: 10.1117/1.JBO.26.2.022910] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Tokuoka Y, Yamada TG, Mashiko D, Ikeda Z, Hiroi NF, Kobayashi TJ, Yamagata K, Funahashi A. 3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis. NPJ Syst Biol Appl 2020;6:32. [PMID: 33082352 DOI: 10.1038/s41540-020-00152-8] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 7.0] [Reference Citation Analysis]
11 Xie Y, Xing F, Shi X, Kong X, Su H, Yang L. Efficient and robust cell detection: A structured regression approach. Med Image Anal 2018;44:245-54. [PMID: 28797548 DOI: 10.1016/j.media.2017.07.003] [Cited by in Crossref: 45] [Cited by in F6Publishing: 18] [Article Influence: 11.3] [Reference Citation Analysis]
12 Rączkowski Ł, Możejko M, Zambonelli J, Szczurek E. ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning. Sci Rep 2019;9:14347. [PMID: 31586139 DOI: 10.1038/s41598-019-50587-1] [Cited by in Crossref: 22] [Cited by in F6Publishing: 12] [Article Influence: 11.0] [Reference Citation Analysis]
13 M M, P S. MRI Brain Tumour Segmentation Using Hybrid Clustering and Classification by Back Propagation Algorithm. Asian Pac J Cancer Prev 2018;19:3257-63. [PMID: 30486629 DOI: 10.31557/APJCP.2018.19.11.3257] [Cited by in Crossref: 10] [Cited by in F6Publishing: 1] [Article Influence: 3.3] [Reference Citation Analysis]
14 Jung H, Lodhi B, Kang J. An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images. BMC Biomed Eng 2019;1:24. [PMID: 32903361 DOI: 10.1186/s42490-019-0026-8] [Cited by in Crossref: 13] [Cited by in F6Publishing: 4] [Article Influence: 6.5] [Reference Citation Analysis]
15 Munir K, Elahi H, Ayub A, Frezza F, Rizzi A. Cancer Diagnosis Using Deep Learning: A Bibliographic Review. Cancers (Basel). 2019;11:1235. [PMID: 31450799 DOI: 10.3390/cancers11091235] [Cited by in Crossref: 67] [Cited by in F6Publishing: 25] [Article Influence: 33.5] [Reference Citation Analysis]
16 Saha M, Chakraborty C, Arun I, Ahmed R, Chatterjee S. An Advanced Deep Learning Approach for Ki-67 Stained Hotspot Detection and Proliferation Rate Scoring for Prognostic Evaluation of Breast Cancer. Sci Rep 2017;7:3213. [PMID: 28607456 DOI: 10.1038/s41598-017-03405-5] [Cited by in Crossref: 47] [Cited by in F6Publishing: 25] [Article Influence: 11.8] [Reference Citation Analysis]
17 Sornapudi S, Stanley RJ, Stoecker WV, Almubarak H, Long R, Antani S, Thoma G, Zuna R, Frazier SR. Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels. J Pathol Inform 2018;9:5. [PMID: 29619277 DOI: 10.4103/jpi.jpi_74_17] [Cited by in Crossref: 28] [Cited by in F6Publishing: 15] [Article Influence: 9.3] [Reference Citation Analysis]
18 Chen M, Zhang B, Topatana W, Cao J, Zhu H, Juengpanich S, Mao Q, Yu H, Cai X. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning. NPJ Precis Oncol. 2020;4:14. [PMID: 32550270 DOI: 10.1038/s41698-020-0120-3] [Cited by in Crossref: 20] [Cited by in F6Publishing: 16] [Article Influence: 20.0] [Reference Citation Analysis]
19 Caicedo JC, Roth J, Goodman A, Becker T, Karhohs KW, Broisin M, Molnar C, McQuin C, Singh S, Theis FJ, Carpenter AE. Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images. Cytometry A 2019;95:952-65. [PMID: 31313519 DOI: 10.1002/cyto.a.23863] [Cited by in Crossref: 81] [Cited by in F6Publishing: 50] [Article Influence: 40.5] [Reference Citation Analysis]
20 Bai J, Jiang H, Li S, Ma X. NHL Pathological Image Classification Based on Hierarchical Local Information and GoogLeNet-Based Representations. Biomed Res Int 2019;2019:1065652. [PMID: 31016181 DOI: 10.1155/2019/1065652] [Cited by in Crossref: 9] [Cited by in F6Publishing: 2] [Article Influence: 4.5] [Reference Citation Analysis]
21 Khoshdeli M, Winkelmaier G, Parvin B. Fusion of encoder-decoder deep networks improves delineation of multiple nuclear phenotypes. BMC Bioinformatics 2018;19:294. [PMID: 30086715 DOI: 10.1186/s12859-018-2285-0] [Cited by in Crossref: 10] [Cited by in F6Publishing: 2] [Article Influence: 3.3] [Reference Citation Analysis]
22 Abdolhoseini M, Kluge MG, Walker FR, Johnson SJ. Segmentation of Heavily Clustered Nuclei from Histopathological Images. Sci Rep 2019;9:4551. [PMID: 30872619 DOI: 10.1038/s41598-019-38813-2] [Cited by in Crossref: 14] [Cited by in F6Publishing: 5] [Article Influence: 7.0] [Reference Citation Analysis]
23 Biswas S, Barma S. A large-scale optical microscopy image dataset of potato tuber for deep learning based plant cell assessment. Sci Data 2020;7:371. [PMID: 33110087 DOI: 10.1038/s41597-020-00706-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
24 Zhang P, Wang F, Teodoro G, Liang Y, Roy M, Brat D, Kong J. Effective nuclei segmentation with sparse shape prior and dynamic occlusion constraint for glioblastoma pathology images. J Med Imaging (Bellingham) 2019;6:017502. [PMID: 30891467 DOI: 10.1117/1.JMI.6.1.017502] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
25 Puttagunta M, Ravi S. Medical image analysis based on deep learning approach. Multimed Tools Appl 2021;:1-34. [PMID: 33841033 DOI: 10.1007/s11042-021-10707-4] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
26 Thillaikkarasi R, Saravanan S. An Enhancement of Deep Learning Algorithm for Brain Tumor Segmentation Using Kernel Based CNN with M-SVM. J Med Syst 2019;43:84. [PMID: 30810822 DOI: 10.1007/s10916-019-1223-7] [Cited by in Crossref: 18] [Cited by in F6Publishing: 6] [Article Influence: 9.0] [Reference Citation Analysis]
27 He B, Lu Q, Lang J, Yu H, Peng C, Bing P, Li S, Zhou Q, Liang Y, Tian G. A New Method for CTC Images Recognition Based on Machine Learning.Front Bioeng Biotechnol. 2020;8:897. [PMID: 32850745 DOI: 10.3389/fbioe.2020.00897] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
28 Xing F, Shi X, Zhang Z, Cai J, Xie Y, Yang L. Transfer Shape Modeling Towards High-throughput Microscopy Image Segmentation. Med Image Comput Comput Assist Interv 2016;9902:183-90. [PMID: 27924318 DOI: 10.1007/978-3-319-46726-9_22] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 0.6] [Reference Citation Analysis]
29 Kost H, Homeyer A, Molin J, Lundström C, Hahn HK. Training Nuclei Detection Algorithms with Simple Annotations. J Pathol Inform 2017;8:21. [PMID: 28584683 DOI: 10.4103/jpi.jpi_3_17] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
30 Xu J, Gong L, Wang G, Lu C, Gilmore H, Zhang S, Madabhushi A. Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images. J Med Imaging (Bellingham) 2019;6:017501. [PMID: 30840729 DOI: 10.1117/1.JMI.6.1.017501] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]