Phân loại suy giảm thính lực thần kinh qua deep-HLNet và học ít số lượng mẫu

Altaf F, Islam SMS, Akhtar N, Janjua NK (2019) Going deep in medical image analysis: Concepts, methods, challenges, and future directions. IEEE Access 7:99540–99572 Antoniou A, Storkey A, Edwards H (2017) Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340 Bahadure NB, Ray AK, Thethi HP (2017) Image analysis for mri based brain tumor detection and feature extraction using biologically inspired bwt and svm. Int J Biomed Imaging 2017:12. https://doi.org/10.1155/2017/9749108 Breininger K, Albarqouni S, Kurzendorfer T, Pfister M, Kowarschik M, Maier A (2018) Intraoperative stent segmentation in x-ray fluoroscopy for endovascular aortic repair. Int J Comput Assist Radiol Surg 13(8):1221–1231 Cai W, Wei Z (2020) Piigan: generative adversarial networks for pluralistic image inpainting arXiv: Computer Vision and Pattern Recognition Chen Y, Yang M, Chen X, Liu B, Wang H, Wang S (2018) Sensorineural hearing loss detection via discrete wavelet transform and principal component analysis combined with generalized eigenvalue proximal support vector machine and tikhonov regularization. Multimed Tools Appl 77(3):3775–3793 Chen Z, Yanwei F, Zhang Y, Jiang Y-G, Xue X, Sigal L (2019) Multi-level semantic feature augmentation for one-shot learning. IEEE Trans Image Process 28(9):4594–4605 Eklund A, Dufort P, Forsberg D, LaConte SM (2013) Medical image processing on the gpu–past, present and future. Med Image Anal 17(8):1073–1094 Fan G, Peng L, Hong W, Sun F (2016) Electric load forecasting by the svr model with differential empirical mode decomposition and auto regression. Neurocomputing 173:958–970 Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning (PMLR) 70:1126–1135 He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, pp 770–778 Hong W (2011) Electric load forecasting by seasonal recurrent svr (support vector regression) with chaotic artificial bee colony algorithm. Energy 36(9):5568–5578 Hong W, Dong Y, Zhang WY, Chen L, Panigrahi BK (2013) Cyclic electric load forecasting by seasonal svr with chaotic genetic algorithm. Int J Electr Power Energy Syst 44(1):604–614 Hosseini-Asl E, Gimel’farb G, and El-Baz A (2016) Alzheimer’s disease diagnostics by a deeply supervised adaptable 3d convolutional network. arXiv preprint arXiv:1607.00556 Kim E, Corte-Real M, Baloch Z (2016) A deep semantic mobile application for thyroid cytopathology. In Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, vol 9789. International Society for Optics and Photonics, pp 97890A Kim J, Kim T, Kim S, Yoo CD (2019) Edge-labeling graph neural network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 11–20 Lee K, Maji S, Ravichandran A, Soatto S (2019) Meta-learning with differentiable convex optimization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 10657–10665 Li M, Geng J, Hong W, Zhang L (2019) Periodogram estimation based on lssvr-ccpso compensation for forecasting ship motion. Nonlinear Dyn 97(4):2579–2594 Pereira A (2017). Hu Moment invariant: a new method for hearing loss detection. In: Proceedings of the 2017 International Conference Advanced Engineering and Technology Research (AETR 2017), Xi'an, China. Atlantis Press, Amsterdam, pp 421–416 Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88 Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng D (2014) Early diagnosis of alzheimer’s disease with deep learning. In: 2014 IEEE 11th international symposium on biomedical imaging (ISBI), Beijing, China. pp 1015–1018. https://doi.org/10.1109/ISBI.2014.6868045 Medela A, Picon A, Saratxaga CL, Belar O, Cabezón V, Cicchi R, Bilbao R, Glover B Few shot learning in histopathological images: reducing the need of labeled data on biological datasets. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, pp 2019, 1860–1864 Mondal AK, Dolz J, Desrosiers C (2018) Few-shot 3d multi-modal medical image segmentation using generative adversarial learning. arXiv preprint arXiv:1810.12241 Muus JS, Weir FW, Kreicher KL, Bowlby DA, Discolo CM, Meyer TA (2017) Hearing loss in children with growth hormone deficiency. Int J Pediatr Otorhinolaryngol 100:107–113 Nayeem A (2017) Hearing loss detection based on wavelet entropy and genetic algorithm. Adv Intell Syst Res 153:49–53 Payan A, Montana G (2015) Predicting alzheimer’s disease: a neuroimaging study with 3d convolutional neural networks. arXiv preprint arXiv:1502.02506 Ravi S, Larochelle H (2016) Optimization as a model for few-shot learning. In: International Conference on Learning Representations (ICLR), Toulon, France Razzak MI, Naz S, Zaib A (2018) Deep learning for medical image processing: Overview, challenges and the future. In: Dey N, Ashour A, Borra S (eds) Classification in BioApps. Lecture Notes in Computational Vision and Biomechanics, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-65981-7_12 Ren M, Triantafillou E, Ravi S, Snell J, Swersky K, Tenenbaum JB, Larochelle H, Zemel RS (2018) Meta-learning for semi-supervised few-shot classification. arXiv preprint arXiv:1803.00676 RPK P, Lamata P, Montana G (2016) Recurrent fully convolutional neural networks for multi-slice mri cardiac segmentation. In: Reconstruction, segmentation, and analysis of medical images. Springer, pp 83–94 Santoro A, Bartunov S, Botvinick M, Wierstra D, and Lillicrap T (2016) One-shot learning with memory-augmented neural networks. arXiv preprint arXiv:1605.06065 Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):60 Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. In: Advances in neural information processing systems 30(NIPS), Long Beach, CA, pp 4077–4087 Sung F, Yang Y, Zhang L, Xiang T, Torr PHS, Hospedales TM (2018) Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, pp 1199–1208 Tang Y (2013) Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239 Vapnik V (2013) The nature of statistical learning theory. Springer science & business media. New York. https://doi.org/10.1007/978-1-4757-3264-1 Vinyals O, Blundell C, Lillicrap T, Wierstra D et al (2016) Matching networks for one shot learning. In: Advances in neural information processing systems, pp 3630–3638 Wang S, Zhang Y, Yang M, Liu B, Ramirez J, Gorriz JM (2017) Preliminary study on unilateral sensorineural hearing loss identification via dual-tree complex wavelet transform and multinomial logistic regression. In: Ferrández VJ, Álvarez-Sánchez J, de la Paz López F, Toledo Moreo J, Adeli H (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science, vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_28 Wang S (2017) Hearing loss detection in medical multimedia data by discrete wavelet packet entropy and single-hidden layer neural network trained by adaptive learning-rate back propagation. In: Cong F, Leung A, Wei Q (eds) Advances in Neural Networks ISNN 2017. Lecture Notes in Computer Science, vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_63 Wang S, Yang M, Li J, Wu X, Wang H, Liu B, Dong Z, Zhang Y (2017) Texture analysis method based on fractional fourier entropy and fitness-scaling adaptive genetic algorithm for detecting left-sided and right-sided sensorineural hearing loss. Fundam Inf 151(1–4):505–521 Wang S-H, Hong J, Yang M (2018) Sensorineural hearing loss identification via nine-layer convolutional neural network with batch normalization and dropout. Multimed Tools Appl:1–16 Wang Y, Girshick R, Hebert M, Hariharan B (2018) Low-shot learning from imaginary data. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) Salt Lake City, pp 7278–7286 Wang Y, Yao Q, Kwok J, Ni LM (2019) Generalizing from a few examples: A survey on few-shot learning. arXiv preprint arXiv:1904.05046 Wang S, Yang M, Liu S, Zhang Y (2020) Sensorineural hearing loss identification via discrete wavelet packet entropy and cat swarm optimization. In: Dey N, Ashour A, Bhattacharyya S (eds) Applied Nature-Inspired Computing: Algorithms and Case Studies. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-13-9263-4_6 You H, Tian S, Yu L, Lv Y (2020) Pixel-level remote sensing image recognition based on bidirectional word vectors. IEEE Trans Geosci Remote Sens 58(2):1281–1293 Zhang Z, Hong W (2019) Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm. Nonlinear Dyn 98(4):1107–1136. https://doi.org/10.1007/s11071-019-05252-7 Zhang Z, Hong W, Li J (2020) Electric load forecasting by hybrid self-recurrent support vector regression model with variational mode decomposition and improved cuckoo search algorithm. IEEE Access 8:14642–14658 Zhao A, Balakrishnan G, Durand F, Guttag JV, Dalca AV (2019) Data augmentation using learned transformations for one-shot medical image segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Long Beach, pp 8543–8553