MNT-DeepSL: Median nerve tracking from carpal tunnel ultrasound images with deep similarity learning and analysis on continuous wrist motions
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Ahmed, 2015, An improved deep learning architecture for person re-identification, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3908, 10.1109/CVPR.2015.7299016
Billakota, 2017, Standard median nerve ultrasound in carpal tunnel syndrome: a retrospective review of 1,021 cases, Clin. Neurophysiol. Pract., 2, 188, 10.1016/j.cnp.2017.07.004
Britz, 1995, Carpal tunnel syndrome: correlation of magnetic resonance imaging, clinical, electrodiagnostic, and intraoperative findings, Neurosurgery, 37, 1097, 10.1227/00006123-199512000-00009
Bromley, 1993, Signature verification using a "Siamese" time delay neural network, 737
Burton, 2014, Diagnosing and managing carpal tunnel syndrome in primary care, Br. J. Gen. Pract., 64, 262, 10.3399/bjgp14X679903
Canziani, 2016, An analysis of deep neural network models for practical applications, CoRR
Dilley, 2003, Quantitative in vivo studies of median nerve sliding in response to wrist, elbow, shoulder and neck movements, Clin. Biomech. (Bristol, Avon), 18, 899, 10.1016/S0268-0033(03)00176-1
Duncan, 1999, Sonography in the diagnosis of carpal tunnel syndrome, Am. J. Roentgenol., 173, 681, 10.2214/ajr.173.3.10470903
Fowler, 2014, Comparison of ultrasound and electrodiagnostic testing for diagnosis of carpal tunnel syndrome: study using a validated clinical tool as the reference standard, J. Bone Jt. Surg. Am., 96, e148, 10.2106/JBJS.M.01250
Fowler, 2015, A comparison of three diagnostic tests for carpal tunnel syndrome using latent class analysis, J. Bone Jt. Surg. Am., 97, 1958, 10.2106/JBJS.O.00476
Glorot, 2010, Understanding the difficulty of training deep feedforward neural networks, 249
He, 2015, Deep residual learning for image recognition, CoRR
John, 1983, CT of carpal tunnel syndrome, AJNR Am. J. Neuroradiol., 4, 770
Keles, 2005, Diagnostic precision of ultrasonography in patients with carpal tunnel syndrome, Am. J. Phys. Med. Rehabil., 84, 443, 10.1097/01.phm.0000163715.11645.96
Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, 1097
Lecun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791
Loh, 2018, Deformation of the median nerve at different finger postures and wrist angles, PeerJ, 6, e5406, 10.7717/peerj.5406
Mondelli, 2002, Carpal tunnel syndrome incidence in a general population, Neurology, 58, 289, 10.1212/WNL.58.2.289
Sabour, 2017, Dynamic routing between capsules, CoRR
Simonyan, 2014, Very deep convolutional networks for large-scale image recognition, CoRR
Simo-Serra, 2015, Discriminative learning of deep convolutional feature point descriptors, 2015 IEEE International Conference on Computer Vision (ICCV), 118, 10.1109/ICCV.2015.22
Subramaniam, 2016, Deep neural networks with inexact matching for person re-identification, 2675
Szabo, 1999, The value of diagnostic testing in carpal tunnel syndrome, J. Hand Surg., 24, 704, 10.1053/jhsu.1999.0704
Vahed, 2018, Ultrasound as a diagnostic tool in the investigation of patients with carpal tunnel syndrome, Eur. J. Transl. Myol., 28, 7380
Vo, 2016, Localizing and orienting street views using overhead imagery, CoRR
Wong, 2004, Carpal tunnel syndrome: diagnostic usefulness of sonography, Radiology, 232, 93, 10.1148/radiol.2321030071
Yoshii, 2009, Ultrasound assessment of the displacement and deformation of the median nerve in the human carpal tunnel with active finger motion, J. Bone Jt. Surg. Am., 91, 2922, 10.2106/JBJS.H.01653