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For: Aatresh AA, Alabhya K, Lal S, Kini J, Saxena PUP. LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images. Int J Comput Assist Radiol Surg 2021;16:1549-63. [PMID: 34053009 DOI: 10.1007/s11548-021-02410-4] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
Number Citing Articles
1 Anusua Basu, Pradip Senapati, Mainak Deb, Rebika Rai, Krishna Gopal Dhal. A survey on recent trends in deep learning for nucleus segmentation from histopathology images. Evolving Systems 2023. [ DOI: 10.1007/s12530-023-09491-3] [Reference Citation Analysis]
2 Cinar U, Cetin Atalay R, Cetin YY. Human Hepatocellular Carcinoma Classification from H&E Stained Histopathology Images with 3D Convolutional Neural Networks and Focal Loss Function. J Imaging 2023;9. [PMID: 36826944 DOI: 10.3390/jimaging9020025] [Reference Citation Analysis]
3 Kadirappa R, Subbian D, Ramasamy P, Ko S. Histopathological carcinoma classification using parallel, cross‐concatenated and grouped convolutions deep neural network. Int J Imaging Syst Tech 2023. [DOI: 10.1002/ima.22846] [Reference Citation Analysis]
4 Adeoye J, Akinshipo A, Koohi-moghadam M, Thomson P, Su Y. Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review. Front Oncol 2022;12. [DOI: 10.3389/fonc.2022.976168] [Reference Citation Analysis]
5 Lee SH, Jang HJ. Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology. Clin Mol Hepatol 2022;28:754-72. [PMID: 35443570 DOI: 10.3350/cmh.2021.0394] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
6 Othman E, Mahmoud M, Dhahri H, Abdulkader H, Mahmood A, Ibrahim M. Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models. Sensors 2022;22:5429. [DOI: 10.3390/s22145429] [Reference Citation Analysis]
7 Sheikh TS, Kim J, Shim J, Cho M. Unsupervised Learning Based on Multiple Descriptors for WSIs Diagnosis. Diagnostics 2022;12:1480. [DOI: 10.3390/diagnostics12061480] [Reference Citation Analysis]
8 Nam D, Chapiro J, Paradis V, Seraphin TP, Kather JN. Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction. JHEP Reports 2022. [DOI: 10.1016/j.jhepr.2022.100443] [Cited by in Crossref: 10] [Cited by in F6Publishing: 4] [Article Influence: 10.0] [Reference Citation Analysis]
9 Peng J, Huang J, Huang G, Zhang J. Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning. Front Oncol 2021;11:730282. [PMID: 34745952 DOI: 10.3389/fonc.2021.730282] [Cited by in F6Publishing: 1] [Reference Citation Analysis]