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For: Fu M, Wu W, Hong X, Liu Q, Jiang J, Ou Y, Zhao Y, Gong X. Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images. BMC Syst Biol. 2018;12:56. [PMID: 29745840 DOI: 10.1186/s12918-018-0572-z] [Cited by in Crossref: 32] [Cited by in F6Publishing: 18] [Article Influence: 10.7] [Reference Citation Analysis]
Number Citing Articles
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2 Barat M, Chassagnon G, Dohan A, Gaujoux S, Coriat R, Hoeffel C, Cassinotto C, Soyer P. Artificial intelligence: a critical review of current applications in pancreatic imaging. Jpn J Radiol 2021;39:514-23. [PMID: 33550513 DOI: 10.1007/s11604-021-01098-5] [Reference Citation Analysis]
3 Ariji Y, Yanashita Y, Kutsuna S, Muramatsu C, Fukuda M, Kise Y, Nozawa M, Kuwada C, Fujita H, Katsumata A, Ariji E. Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique. Oral Surg Oral Med Oral Pathol Oral Radiol 2019;128:424-30. [PMID: 31320299 DOI: 10.1016/j.oooo.2019.05.014] [Cited by in Crossref: 42] [Cited by in F6Publishing: 23] [Article Influence: 21.0] [Reference Citation Analysis]
4 Kumar H, Desouza SV, Petrov MS. Automated pancreas segmentation from computed tomography and magnetic resonance images: A systematic review. Computer Methods and Programs in Biomedicine 2019;178:319-28. [DOI: 10.1016/j.cmpb.2019.07.002] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 4.5] [Reference Citation Analysis]
5 Zhong Y, Vinogradskiy Y, Chen L, Myziuk N, Castillo R, Castillo E, Guerrero T, Jiang S, Wang J. Technical Note: Deriving ventilation imaging from 4DCT by deep convolutional neural network. Med Phys 2019;46:2323-9. [PMID: 30714159 DOI: 10.1002/mp.13421] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
6 Li Q, Yu Z, Wang Y, Zheng H. TumorGAN: A Multi-Modal Data Augmentation Framework for Brain Tumor Segmentation. Sensors (Basel) 2020;20:E4203. [PMID: 32731598 DOI: 10.3390/s20154203] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 8.0] [Reference Citation Analysis]
7 Mendoza Ladd A, Diehl DL. Artificial intelligence for early detection of pancreatic adenocarcinoma: The future is promising. World J Gastroenterol 2021;27:1283-95. [PMID: 33833482 DOI: 10.3748/wjg.v27.i13.1283] [Reference Citation Analysis]
8 Zhou T, Ruan S, Canu S. A review: Deep learning for medical image segmentation using multi-modality fusion. Array 2019;3-4:100004. [DOI: 10.1016/j.array.2019.100004] [Cited by in Crossref: 79] [Cited by in F6Publishing: 10] [Article Influence: 39.5] [Reference Citation Analysis]
9 Yan Y, Zhang D. Multi-scale U-like network with attention mechanism for automatic pancreas segmentation. PLoS One 2021;16:e0252287. [PMID: 34043732 DOI: 10.1371/journal.pone.0252287] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Li S, Xiao J, He L, Peng X, Yuan X. The Tumor Target Segmentation of Nasopharyngeal Cancer in CT Images Based on Deep Learning Methods. Technol Cancer Res Treat. 2019;18:1533033819884561. [PMID: 31736433 DOI: 10.1177/1533033819884561] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 8.0] [Reference Citation Analysis]
11 Enriquez JS, Chu Y, Pudakalakatti S, Hsieh KL, Salmon D, Dutta P, Millward NZ, Lurie E, Millward S, McAllister F, Maitra A, Sen S, Killary A, Zhang J, Jiang X, Bhattacharya PK, Shams S. Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer. JMIR Med Inform 2021;9:e26601. [PMID: 34137725 DOI: 10.2196/26601] [Reference Citation Analysis]
12 Li J, Lin X, Che H, Li H, Qian X. Pancreas segmentation with probabilistic map guided bi-directional recurrent UNet. Phys Med Biol 2021;66. [PMID: 33915526 DOI: 10.1088/1361-6560/abfce3] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
13 Wang Z, Chang Y, Peng Z, Lv Y, Shi W, Wang F, Pei X, Xu XG. Evaluation of deep learning-based auto-segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients. J Appl Clin Med Phys 2020;21:272-9. [PMID: 33238060 DOI: 10.1002/acm2.13097] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
14 Zheng H, Chen Y, Yue X, Ma C, Liu X, Yang P, Lu J. Deep pancreas segmentation with uncertain regions of shadowed sets. Magn Reson Imaging. 2020;68:45-52. [PMID: 31987903 DOI: 10.1016/j.mri.2020.01.008] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 9.0] [Reference Citation Analysis]
15 Xu J, Jing M, Wang S, Yang C, Chen X. A review of medical image detection for cancers in digestive system based on artificial intelligence. Expert Rev Med Devices. 2019;16:877-889. [PMID: 31530047 DOI: 10.1080/17434440.2019.1669447] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
16 Wang X, Chung WY, Correa E, Zhu Y, Issa E, Dennison AR. The integration of artificial intelligence models to augment imaging modalities in pancreatic cancer. Journal of Pancreatology 2020;3:173-80. [DOI: 10.1097/jp9.0000000000000056] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Liu K, Wu T, Chen P, Tsai YM, Roth H, Wu M, Liao W, Wang W. Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation. The Lancet Digital Health 2020;2:e303-13. [DOI: 10.1016/s2589-7500(20)30078-9] [Cited by in Crossref: 23] [Cited by in F6Publishing: 6] [Article Influence: 23.0] [Reference Citation Analysis]
18 Bagheri MH, Roth H, Kovacs W, Yao J, Farhadi F, Li X, Summers RM. Technical and Clinical Factors Affecting Success Rate of a Deep Learning Method for Pancreas Segmentation on CT. Acad Radiol 2020;27:689-95. [PMID: 31537506 DOI: 10.1016/j.acra.2019.08.014] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 3.5] [Reference Citation Analysis]