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Cited by in F6Publishing
For: Kurc T, Bakas S, Ren X, Bagari A, Momeni A, Huang Y, Zhang L, Kumar A, Thibault M, Qi Q, Wang Q, Kori A, Gevaert O, Zhang Y, Shen D, Khened M, Ding X, Krishnamurthi G, Kalpathy-Cramer J, Davis J, Zhao T, Gupta R, Saltz J, Farahani K. Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches. Front Neurosci 2020;14:27. [PMID: 32153349 DOI: 10.3389/fnins.2020.00027] [Cited by in Crossref: 20] [Cited by in F6Publishing: 9] [Article Influence: 10.0] [Reference Citation Analysis]
Number Citing Articles
1 Paijens ST, Vledder A, de Bruyn M, Nijman HW. Tumor-infiltrating lymphocytes in the immunotherapy era. Cell Mol Immunol 2021;18:842-59. [PMID: 33139907 DOI: 10.1038/s41423-020-00565-9] [Cited by in Crossref: 20] [Cited by in F6Publishing: 23] [Article Influence: 10.0] [Reference Citation Analysis]
2 Sasank VVS, Venkateswarlu S. Hybrid deep neural network with adaptive rain optimizer algorithm for multi-grade brain tumor classification of MRI images. Multimed Tools Appl. [DOI: 10.1007/s11042-022-12106-9] [Reference Citation Analysis]
3 Im S, Hyeon J, Rha E, Lee J, Choi HJ, Jung Y, Kim TJ. Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning. Sensors (Basel) 2021;21:3500. [PMID: 34067934 DOI: 10.3390/s21103500] [Reference Citation Analysis]
4 Crouzet C, Jeong G, Chae RH, LoPresti KT, Dunn CE, Xie DF, Agu C, Fang C, Nunes ACF, Lau WL, Kim S, Cribbs DH, Fisher M, Choi B. Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages. Sci Rep 2021;11:10725. [PMID: 34021170 DOI: 10.1038/s41598-021-88236-1] [Reference Citation Analysis]
5 Singh G, Manjila S, Sakla N, True A, Wardeh AH, Beig N, Vaysberg A, Matthews J, Prasanna P, Spektor V. Radiomics and radiogenomics in gliomas: a contemporary update. Br J Cancer 2021;125:641-57. [PMID: 33958734 DOI: 10.1038/s41416-021-01387-w] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
6 Liu D, Chen J, Hu X, Yang K, Liu Y, Hu G, Ge H, Zhang W, Liu H. Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures. Front Oncol 2021;11:699265. [PMID: 34295824 DOI: 10.3389/fonc.2021.699265] [Reference Citation Analysis]
7 Parwani AV, Amin MB. Convergence of Digital Pathology and Artificial Intelligence Tools in Anatomic Pathology Practice: Current Landscape and Future Directions. Adv Anat Pathol 2020;27:221-6. [PMID: 32541593 DOI: 10.1097/PAP.0000000000000271] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
8 Darrigues E, Elberson BW, De Loose A, Lee MP, Green E, Benton AM, Sink LG, Scott H, Gokden M, Day JD, Rodriguez A. Brain Tumor Biobank Development for Precision Medicine: Role of the Neurosurgeon. Front Oncol 2021;11:662260. [PMID: 33981610 DOI: 10.3389/fonc.2021.662260] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
9 Wewetzer L, Held LA, Steinhäuser J. Diagnostic performance of deep-learning-based screening methods for diabetic retinopathy in primary care-A meta-analysis. PLoS One 2021;16:e0255034. [PMID: 34375355 DOI: 10.1371/journal.pone.0255034] [Reference Citation Analysis]
10 Nanaa A, Akkus Z, Lee WY, Pantanowitz L, Salama ME. Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders. Pathology 2021;53:400-7. [PMID: 33642096 DOI: 10.1016/j.pathol.2020.12.004] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]