BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Wang X, Wang D, Yao Z, Xin B, Wang B, Lan C, Qin Y, Xu S, He D, Liu Y. Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations. Front Neurosci 2018;12:1046. [PMID: 30686996 DOI: 10.3389/fnins.2018.01046] [Cited by in Crossref: 19] [Cited by in F6Publishing: 16] [Article Influence: 6.3] [Reference Citation Analysis]
Number Citing Articles
1 Wang L, Wang S, Chen R, Qu X, Chen Y, Huang S, Liu C. Nested Dilation Networks for Brain Tumor Segmentation Based on Magnetic Resonance Imaging. Front Neurosci. 2019;13:285. [PMID: 31024229 DOI: 10.3389/fnins.2019.00285] [Cited by in Crossref: 8] [Cited by in F6Publishing: 3] [Article Influence: 2.7] [Reference Citation Analysis]
2 Gonçalves FG, Chawla S, Mohan S. Emerging MRI Techniques to Redefine Treatment Response in Patients With Glioblastoma. J Magn Reson Imaging 2020;52:978-97. [PMID: 32190946 DOI: 10.1002/jmri.27105] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
3 Truong AH, Sharmanska V, Limbӓck-Stanic C, Grech-Sollars M. Optimization of deep learning methods for visualization of tumor heterogeneity and brain tumor grading through digital pathology. Neurooncol Adv 2020;2:vdaa110. [PMID: 33196039 DOI: 10.1093/noajnl/vdaa110] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
4 Fan Y, Li Y, Bao X, Zhu H, Lu L, Yao Y, Li Y, Su M, Feng F, Feng S, Feng M, Wang R. Development of Machine Learning Models for Predicting Postoperative Delayed Remission in Patients With Cushing's Disease. J Clin Endocrinol Metab 2021;106:e217-31. [PMID: 33000120 DOI: 10.1210/clinem/dgaa698] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
5 Zhuge Y, Ning H, Mathen P, Cheng JY, Krauze AV, Camphausen K, Miller RW. Automated glioma grading on conventional MRI images using deep convolutional neural networks. Med Phys 2020;47:3044-53. [PMID: 32277478 DOI: 10.1002/mp.14168] [Cited by in Crossref: 16] [Cited by in F6Publishing: 8] [Article Influence: 8.0] [Reference Citation Analysis]
6 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]
7 Huang J, Xin B, Wang X, Qi Z, Dong H, Li K, Zhou Y, Lu J. Multi-parametric MRI phenotype with trustworthy machine learning for differentiating CNS demyelinating diseases. J Transl Med 2021;19:377. [PMID: 34488799 DOI: 10.1186/s12967-021-03015-w] [Reference Citation Analysis]
8 Hein AL, Mukherjee M, Talmon GA, Natarajan SK, Nordgren TM, Lyden E, Hanson CK, Cox JL, Santiago-Pintado A, Molani MA, Ormer MV, Thompson M, Thoene M, Akhter A, Anderson-Berry A, Yuil-Valdes AG. QuPath Digital Immunohistochemical Analysis of Placental Tissue. J Pathol Inform 2021;12:40. [PMID: 34881095 DOI: 10.4103/jpi.jpi_11_21] [Reference Citation Analysis]
9 Sulu C, Bektaş AB, Şahin S, Durcan E, Kara Z, Demir AN, Özkaya HM, Tanrıöver N, Çomunoğlu N, Kızılkılıç O, Gazioğlu N, Gönen M, Kadıoğlu P. Machine learning as a clinical decision support tool for patients with acromegaly. Pituitary. [DOI: 10.1007/s11102-022-01216-0] [Reference Citation Analysis]
10 Ramsdale E, Snyder E, Culakova E, Xu H, Dziorny A, Yang S, Zand M, Anand A. An introduction to machine learning for clinicians: How can machine learning augment knowledge in geriatric oncology? J Geriatr Oncol 2021:S1879-4068(21)00081-3. [PMID: 33795205 DOI: 10.1016/j.jgo.2021.03.012] [Reference Citation Analysis]
11 Bhatele KR, Bhadauria SS. Machine learning application in Glioma classification: review and comparison analysis. Arch Computat Methods Eng 2022;29:247-74. [DOI: 10.1007/s11831-021-09572-z] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Chandra Joshi R, Mishra R, Gandhi P, Pathak VK, Burget R, Dutta MK. Ensemble based machine learning approach for prediction of glioma and multi-grade classification. Comput Biol Med 2021;137:104829. [PMID: 34508971 DOI: 10.1016/j.compbiomed.2021.104829] [Reference Citation Analysis]
13 Gore S, Chougule T, Jagtap J, Saini J, Ingalhalikar M. A Review of Radiomics and Deep Predictive Modeling in Glioma Characterization. Academic Radiology 2020. [DOI: 10.1016/j.acra.2020.06.016] [Cited by in Crossref: 13] [Cited by in F6Publishing: 9] [Article Influence: 6.5] [Reference Citation Analysis]
14 Sotoudeh H, Shafaat O, Bernstock JD, Brooks MD, Elsayed GA, Chen JA, Szerip P, Chagoya G, Gessler F, Sotoudeh E, Shafaat A, Friedman GK. Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine. Front Oncol 2019;9:768. [PMID: 31475111 DOI: 10.3389/fonc.2019.00768] [Cited by in Crossref: 26] [Cited by in F6Publishing: 25] [Article Influence: 8.7] [Reference Citation Analysis]
15 Dai C, Fan Y, Li Y, Bao X, Li Y, Su M, Yao Y, Deng K, Xing B, Feng F, Feng M, Wang R. Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up. Front Endocrinol (Lausanne) 2020;11:643. [PMID: 33042013 DOI: 10.3389/fendo.2020.00643] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Rathore S, Chaddad A, Iftikhar MA, Bilello M, Abdulkadir A. Combining MRI and Histologic Imaging Features for Predicting Overall Survival in Patients with Glioma. Radiol Imaging Cancer 2021;3:e200108. [PMID: 34296969 DOI: 10.1148/rycan.2021200108] [Reference Citation Analysis]
17 Shi H, Yang D, Tang K, Hu C, Li L, Zhang L, Gong T, Cui Y. Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease. Clin Nutr 2021;41:202-10. [PMID: 34906845 DOI: 10.1016/j.clnu.2021.11.006] [Reference Citation Analysis]
18 Williams S, Layard Horsfall H, Funnell JP, Hanrahan JG, Khan DZ, Muirhead W, Stoyanov D, Marcus HJ. Artificial Intelligence in Brain Tumour Surgery-An Emerging Paradigm. Cancers (Basel) 2021;13:5010. [PMID: 34638495 DOI: 10.3390/cancers13195010] [Reference Citation Analysis]
19 Chen TY, Liu Y, Chen L, Luo J, Zhang C, Shen XF. Identification of the potential biomarkers in patients with glioma: a weighted gene co-expression network analysis. Carcinogenesis 2020;41:743-50. [PMID: 31761927 DOI: 10.1093/carcin/bgz194] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
20 Pei L, Jones KA, Shboul ZA, Chen JY, Iftekharuddin KM. Deep Neural Network Analysis of Pathology Images With Integrated Molecular Data for Enhanced Glioma Classification and Grading. Front Oncol 2021;11:668694. [PMID: 34277415 DOI: 10.3389/fonc.2021.668694] [Reference Citation Analysis]
21 Bas J, Zou Z, Cirillo C. An interpretable machine learning approach to understanding the impacts of attitudinal and ridesourcing factors on electric vehicle adoption. Transportation Letters. [DOI: 10.1080/19427867.2021.2009098] [Reference Citation Analysis]