Convolutional neural network in the detection of gastrointestinal tumor and tap

Hohenberger, 2018, Gastrointestinal stromal tumors, Visc. Med., 34, 332, 10.1159/000494077 Shigita, 2017, Long-term outcomes after endoscopic submucosal dissection for superficial colorectal tumors, Gastrointest. Endosc., 85, 546, 10.1016/j.gie.2016.07.044 Takahashi, 2017, Genomic and transcriptomic analysis of imatinib resistance in gastrointestinal stromal tumors, Genes Chromosomes Cancer, 56, 303, 10.1002/gcc.22438 Wen, 2017, Automatic segmentation of liver tumor in CT images with deep convolutional neural networks, J. Comput. Commun., 03, 146 A E H, 2017, Neuroendocrine tumors of the lung: current challenges and advances in the diagnosis and management of well-differentiated disease, J. Thorac. Oncol., 12, 425, 10.1016/j.jtho.2016.11.2222 Li, 2017, PLCB4 copy gain and PLC4 overexpression in primary gastrointestinal stromal tumors: integrative characterization of a lipid-catabolizing enzyme associated with worse disease-free survival, Oncotarget, 8, 19997, 10.18632/oncotarget.15306 Scola, 2017, Getting the GIST: a pictorial review of the various patterns of presentation of gastrointestinal stromal tumors on imaging, Abdom. Radiol., 42, 1350, 10.1007/s00261-016-1025-z Hsu, 2017, Imatinib dose escalation versus sunitinib as a second-line treatment against advanced gastrointestinal stromal tumors: a nationwide population-based cohort study, Oncotarget, 8, 71128, 10.18632/oncotarget.16795 Khoo, 2017, Laparoscopic wedge resection for suspected large (≥5cm) gastric gastrointestinal stromal tumors, Surg. Endosc., 42, 1 Milone, 2017, Safety and efficacy of laparoscopic wedge gastrectomy for large gastrointestinal stromal tumors - ScienceDirect, Eur. J. Surg. Oncol. (EJSO), 43, 796, 10.1016/j.ejso.2017.01.005 Oppelt, 2017, Gastrointestinal stromal tumors (GISTs): point mutations matter in management, a review, J. Gastrointest. Oncol., 8, 466, 10.21037/jgo.2016.09.15 Finkelstein, 2017, Pancreatic neuroendocrine tumors (panNETs): analysis of overall survival of nonsurgical management versus surgical resection, J. Gastrointest. Surg., 21, 1, 10.1007/s11605-017-3365-6 Schirrmeister, 2017, Deep learning with convolutional neural networks for decoding and visualization of EEG pathology, Hum. Brain Mapp., 38, 5391, 10.1002/hbm.23730 Lu, 2017, Automatic 3D liver location and segmentation via convolutional neural network and graph cut, Int. J. Comput. Assist. Radiol. Surg., 12, 171, 10.1007/s11548-016-1467-3 Alshehhi, 2017, Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks, ISPRS J. Photogramm. Remote. Sens., 130, 139, 10.1016/j.isprsjprs.2017.05.002 Hawkins, 2017, Preoperative chemotherapy and survival for large anorectal gastrointestinal stromal tumors: a national analysis of 333 cases, Ann. Surg. Oncol., 24, 1, 10.1245/s10434-016-5706-1 Akahoshi, 2018, Current clinical management of gastrointestinal stromal tumor, World J. Gastroenterol., 24, 2806, 10.3748/wjg.v24.i26.2806 Keung, 2017, Management of gastrointestinal stromal tumors, Surg. Clin. North Am., 97, 437, 10.1016/j.suc.2016.12.001 Frilling, 2017, Neuroendocrine tumors of the gastrointestinal tract, Visc. Med., 33, 321, 10.1159/000475539 Wang, 2019, DeepIGeoS: a deep interactive geodesic framework for medical image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 41, 1559, 10.1109/TPAMI.2018.2840695 Khosravi, 2018, Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images, EBioMedicine, 27, 317, 10.1016/j.ebiom.2017.12.026 Xiang, 2017, Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI, Neurocomputing, 267, 406, 10.1016/j.neucom.2017.06.048 Wang, 2018, Alcoholism detection by data augmentation and convolutional neural network with stochastic pooling, J. Med. Syst., 42, 1, 10.1007/s10916-017-0845-x Luo, 2017, Deep learning of graphs with Ngram convolutional neural networks, IEEE Trans. Knowl. Data Eng., 29, 2125, 10.1109/TKDE.2017.2720734 Tellez, 2018, Whole-slide mitosis detection in H&E breast histology using PHH3 as a reference to train distilled stain-invariant convolutional networks, IEEE Trans. Med. Imaging, 37, 2126, 10.1109/TMI.2018.2820199 Zhang, 2018, Convolutional invasion and expansion networks for tumor growth prediction, IEEE Trans. Med. Imaging, 37, 638, 10.1109/TMI.2017.2774044 Akkus, 2017, Predicting deletion of chromosomal arms 1p/19q in low-grade gliomas from MR images using machine intelligence, J. Digit. Imaging, 30, 469, 10.1007/s10278-017-9984-3 Khiyari, 2017, Age invariant face recognition using convolutional neural networks and set distances, J. Inf. Secur., 08, 174