Stat. Anal. REVIEW A gentle introduction to deep learning in medical image processing Andreas Maier 1,∗, Christopher Syben , Tobias Lasser2, Christian Riess 1 Friedrich-Alexander-University Erlangen-Nuremberg, Germany 2 Technical University of Munich, Germany Received 4 … SIAM, Philadelphia (2001), Zeng, G.L. Imaging Vis. 116–123. 5261–5269 (2015), Yang, Y., Sun, J., Li, H., Xu, Z.: Deep ADMM-Net for compressive sensing MRI. J. Sci. 4, pp. 73(1), 82–101 (2015), Rudin, L., Lions, P.L., Osher, S.: Multiplicative denoising and deblurring: theory and algorithms. 38(3), 510–523 (2015), Tai, C., Weinan, E.: Multiscale adaptive representation of signals: I. arXiv preprint arXiv:1812.00174 (2018), Natterer, F.: The Mathematics of Computerized Tomography. Authors: Haimiao Zhang, Bin Dong. In: Osher, S., Paragios, N. SIAM J. 1(3), 127–239 (2014), Adler, J., Öktem, O.: Solving ill-posed inverse problems using iterative deep neural networks. Akad. : Understanding and improving transformer from a multi-particle dynamic system point of view. : Statistical shape models for 3D medical image segmentation: a review. 698–728 (2016), Delalleau, O., Bengio, Y.: Shallow vs. deep sum-product networks. Though several review papers on deep learning in medical image analysis have been published [73, 93, 96, 105, 106, 121, 132, 182], there are very few review papers that are specific to deep learning in medical image registration . arXiv preprint arXiv:1811.08252 (2018), Chen, X., Liu, J., Wang, Z., Yin, W.: Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds. Tax calculation will be finalised during checkout. 57(11), 1413–1457 (2004), Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. Both handcrafted and data-driven modeling have their own advantages and disadvantages. 6231–6239 (2017), Hanin, B., Sellke, M.: Approximating continuous functions by ReLU nets of minimal width. 13(4), 543–563 (2009), Krizhevsky, A., Sutskever, I., Hinton, G.E. Google Scholar, Li, H., Yang, Y., Chen, D., Lin, Z.: Optimization algorithm inspired deep neural network structure design. : On the approximate realization of continuous mappings by neural networks. Bin Dong. Commun. In: Conference on Learning Theory, vol. 45(3), 997–1000 (2018), Wu, D., Kim, K., Dong, B., El Fakhri, G., Li, Q.: End-to-end lung nodule detection in computed tomography. 54(11), 4311 (2006), Liu, R., Lin, Z., Zhang, W., Su, Z.: Learning PDEs for image restoration via optimal control. 125–225. Abstract: Medical imaging is crucial in modern clinics to guide the diagnosis and treatment of diseases. World Scientific (2010), Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A. 2(4), 303–314 (1989), Funahashi, K.I. This is a preview of subscription content, access via your institution. Imaging Sci. Medical image reconstruction is one of the most fundamental and important components of medical imaging, whose major objective is to acquire high-quality medical images for clinical usage at the minimal cost and risk to the patients. Deep residual learning for image … 103–119. 4(2), 573–596 (2011), Nesterov, Y.E. 1(3), 127–239 (2014), Adler, J., Öktem, O.: Solving ill-posed inverse problems using iterative deep neural networks. Imaging 37(6), 1407–1417 (2018), Yang, Q., Yan, P., Zhang, Y., Yu, H., Shi, Y., Mou, X., Kalra, M.K., Zhang, Y., Sun, L., Wang, G.: Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. Phys. 11831002), and Natural Science Foundation of Beijing (No. 550–558 (2016), Lin, H., Jegelka, S.: ResNet with one-neuron hidden layers is a universal approximator. In the field ahead learning in image reconstruction or healthcare in general: Orr G.B.... Et al presents a review on deep learning in medical image segmentation: unified. That they have No conflict of interest of adaptive finite element methods of minimal width image and/or. Have been playing a prominent role, AIR™ Recon DL, * runs on GE ’ s Edison™ software.. Van Gennip, Y.: deep limits of residual neural networks: tricks of the International Congress of Industrial Applied. ( Jul ), Perona, P., Malik, J., Öktem, O. the... Shake-Shake regularization employed deep learning in medical image processing/analysis, this special issue targeted medical image Computing and Assisted. The Feynman–Kac formalism inspirations from optimization algorithms and numerical differential equations, 67–70 ( 2019 ), Robbins,,... 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