Alvarez, J.M. and Salzmann, M. (2017) Compression-aware Training of Deep Networks. [online]http://papers.nips.cc/paper/6687-compression-aware-training-of-deep-networks (Accessed 17 February 2020).
American Cancer Society. [online] https://www.cancer.org/cancer/kidney-cancer/detection-diagnosis-staging/detection.html (Accessed 17 December 2019).
Denil, M., Shakibi, B., Dinh, L., Ranzato M.C. and Freitas, N. (2013)Predicting Parameters in Deep Learning. [online]https://papers.nips.cc/paper/5025-predicting-parameters-in-deep-learning.pdf (accessed on 18 February 2020).
Han, S., Mao, H. and Dally, W.J. (2016) ‘Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding’,International Conference on Learning Representations (ICLR).https://arxiv.org/abs/1510.00149
Han, S., Mao, H. and Dally,W.J. (2016)‘Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization, and Human Coding’. [online]https://arxiv.org/abs/1510.00149 (accessed on 17 February 2020).
Hassibi, B. and Stork, D.G. (1993)Second Order Derivatives for Network Pruning: Optimal Brain Surgeon.[online]https://authors.library.caltech.edu/54983/3/647-second-order-derivatives-for-networkpruning-optimal-brain-surgeon(1).pdf (Accessed 15 February 2020).
He, Y., Zhang, X. and Sun, J. (2017)‘Channel Pruning for Accelerating Very Deep Neural Networks’,2017 IEEE International Conference on Computer Vision (ICCV) pp. 1398-1406, doi: 10.1109/ICCV.2017.155.
He, Y., Lin J., Liu Z., Wang, H., Li L.J. and Han, S. (2018) ‘AMC: AutoMLfor model compression and acceleration on mobile devices”,inProceedings of the European Conference on Computer Vision (ECCV), pp.784–800.
Heller, N.,Sathianathen, N.,Kalapara, A., Walczak, E., Moore, K., Kaluzniak, H., Rosenberg, J., Blake, P.,Rengel, Z.,Oestreich, M., Dean, J.,Tradewell, M., Shah, A.,Tejpaul, R., Edgerton, Z., Peterson, M., Raza, S.,Regmi, S.,Papanikolopoulos, N. and Weight, C. (2019)The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes, arXiv:1904.00445.
Hesamian, M.H., Jia, W., He, X. and Kennedy, P. (2019)‘Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges’, Journal of Digit Imaging,Vol. 32, pp.582. DOI:10.1007/s10278-019-00227-x
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang W., Weyand, T., Andreetto, M. and Adam, H.(2017)MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.[online]https://arxiv.org/abs/1704.04861 (accessed on 18 February 2020).
Karakanis, S. and Leontidis, G. (2020) ‘Lightweight deep learning models for detecting COVID-19 from chest X-ray images’,Computers in Biology and Medicine, Vol. 130, pp.104181. DOI:10.1016/j.compbiomed.2020.104181. Epub ahead of print. PMID: 33360271; PMCID: PMC7831681.
Kehl, W., Tombari, F., Ilic, S. and Navab, N. (2017)‘Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core’,inProceedingsof the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, pp. 465–473.
Kingma,D.P. and Ba, J. (2014)‘Adam: A method for stochastic optimization’, arXiv:1412.6980.
Kutikov, A. and Uzzo, R.G. (2009) ‘The renalnephrometry score: a comprehensive standardized system for quantitating renal tumor size, location and depth’.The Journal of Urology,Vol. 182. No. 3, pp.844–853.
LeCun, Y., Denker, J.S. and Solla, S.A. (1990) ‘Optimal Brain Damage’,Advances in Neural Information Processing Systems,Vol. 2, pp.598–605.
Lee, H.S., Hong, H. and Kim,J. (2017)‘Detection and segmentation of small renal masses in contrast-enhanced CT images using texture and context feature classification’, IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, 2017, pp. 583–586. DOI:10.1109/ISBI.2017.7950588.
Linguraru, M.G., Wang, S., Shah, F., Gautam, R., Peterson, J., Linehan, W. M. and Summers, R.M. (2011)‘Automated non-invasive classification of renal cancer on multiphase CT’, Medical Physics, Vol. 38, No. 10, pp.5738–5746. DOI:10.1118/1.3633898.
Liu, R., Lehman, J., Molino, P., Such, F.P., Frank, E., Sergeev, A. and Yosinski, J. (2018)‘An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution’, arXiv:1807.03247, 2018.
Liu, Z., Sun, M., Zhou, T., Huang, G. and Darrell, T. (2019)‘Rethinking the value of network pruning’, International Conference on Learning Representations (ICLR).
Qin, Z., Zhang, Z., Checn, X., Wang, C. and Peng, Y. (2018) ‘FD-MobileNet: Improved MobileNet with a Fast-Downsampling Strategy’, 2018 25th IEEE International Conference on Image Processing (ICIP), pp.1363-1367
Olaf, R., Fischer, P. andBrox, T. (2015) ‘U-Net: Convolutional Networks for Biomedical Image Segmentation’, In: Navab N., Hornegger J., Wells W., Frangi A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol. 9351. Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4_28
Sabarinathan, D.,Beham, M.P. and Roomi, S.M.Md.M. (2019). ‘Hyper Vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit’, in National Conference on Computer Vision, Pattern Recognition, Image Processing, and Graphics, pp.609–618. Springer, Singapore, 2019.DOI:10.1007/978-981-15-8697-2_57
Shah, B., Sawla, C., Bhanushali, S., Bhogale,P. (2017)‘Kidney Tumor Segmentation and Classification on Abdominal CT Scans’, International Journal of Computer Applications,Vol. 164, No. 9, pp.1–5.
Sharma, K. (2017)‘Machine Learning Methods for Segmentation in Autosomal Dominant Polycystic Kidney Disease’, PhD thesis, Technische Universität München, Munich, Germany.
Shen, W., Wang, X., Wang, Y., Bai, X. and Zhang A. (2015)‘Deepcontour: A deep convolutional feature learned by positive-sharing loss for contour detection’, in Proceedings of theIEEE Conference on Computer Vision and Pattern Recognition,pp.3982–3991.
Skalski, A., Jakubowski, J. and Drewniak,T. (2016)‘Kidney tumor segmentation and detection on Computed Tomography data’, in IEEE International Conference on Imaging Systems and Techniques (IST), Chania, 2016, pp.238–242. DOI:10.1109/IST.2016.7738230
Thong, W., Kadoury, S., Piche,N. and Pal,C.J. (2016)‘Convolutional networks for kidney segmentation in contrast-enhanced CT scans’, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. Vol. 6, No. 3, pp.277–282. DOI:10.1080/21681163.2016.1148636.
Vaseli, H., Liao, Z., Abdi, A.H., Girgis, H., Behnami, D., Luong, C., Dezaki, F.T., Dhungel, N., Rohling, R., Gin, K. and Abolmaesumi, P. (2019) ‘Designing lightweight deep learning models for echocardiography view classification’, in SPIE Medical Imaging, 2019, San Diego, California, United States.
Wang, G., Li, W., Zuluaga, M.A., Pratt, R., Patel, P.A., Aertsen, M., Doel, T., David, A.L., Deprest, J., Ourselin, S. andVercauteren, T. (2018)‘Interactive Medical Image Segmentation Using Deep Learning with Image-Specific Fine Tuning’, IEEE Transactions on Medical Imaging, Vol. 37, No. 7, pp.1562–1573. https://doi.org/10.1109/TMI.2018.2791721
Yang G., Gu, J., Chen, Y., Liu, W., Tang, L., Shu, H. andToumoulin, C. (2014)‘Automatic kidney segmentation in CT images based on multi-atlas image registration’, in 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, 2014, pp.5538–5541. https://doi.org/10.1109/EMBC.2014.6944881
Zhang, X., Zhou, X., Lin, M., Sun,J. (2017) ‘ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices’,inProceedingsIEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, AL, USA, pp.6848–6856.