A gentle introduction to deep learning in medical image processing
Tài liệu tham khảo
LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539
Dahl, 2012, Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition, IEEE Trans Actions Audio Speech Lang Process, 20, 30, 10.1109/TASL.2011.2134090
Krizhevsky, 2012, ImageNET classification with deep convolutional neural networks, 1097
Silver, 2016, Mastering the game of go with deep neural networks and tree search, Nature, 529, 484, 10.1038/nature16961
Mnih, 2015, Human-level control through deep reinforcement learning, Nature, 518, 529, 10.1038/nature14236
Mordvintsev, 2015, 5
Tan, 2017, ArtGAN: artwork synthesis with conditional categorical GANs, 3760
Briot, 2017
Seebock, 2015
Shen, 2017, Deep learning in medical image analysis, Annu Rev Biomed Eng, 19, 221, 10.1146/annurev-bioeng-071516-044442
Pawlowski, 2017
Litjens, 2017, A survey on deep learning in medical image analysis, Med Image Anal, 42, 60, 10.1016/j.media.2017.07.005
Erickson, 2017, Machine learning for medical imaging, Radiographics, 37, 505, 10.1148/rg.2017160130
Suzuki, 2017, Survey of deep learning applications to medical image analysis, Med Imaging Technol, 35, 212
Hagerty, 2017, Medical image processing in the age of deep learning, 306
Lakhani, 2018, Hello world deep learning in medical imaging, J Digit Imaging, 31, 283, 10.1007/s10278-018-0079-6
Kim, 2018, Prospects of deep learning for medical imaging, Precis Future Med, 2, 37, 10.23838/pfm.2018.00030
Ker, 2018, Deep learning applications in medical image analysis, IEEE Access, 6, 9375, 10.1109/ACCESS.2017.2788044
Rajchl, 2018
Breininger, 2018
Cornelisse, 2018
Zhou, 2017
Lu, 2017
Chollet, 2017
Géron, 2017
Sahiner, 2018, Deep learning in medical imaging, Med Phys, 46, e1, 10.1002/mp.13264
Niemann, 2013, vol. 4
Rosenblatt, 1957
Cybenko, 1989, Approximation by superpositions of a sigmoidal function, Math Control Signals Syst, 2, 303, 10.1007/BF02551274
Hornik, 1991, Approximation capabilities of multilayer feedforward networks, Neural Netw, 4, 251, 10.1016/0893-6080(91)90009-T
Bengio, 2013, Representation learning: a review and new perspectives, IEEE Trans Pattern Anal Mach Intell, 35, 1798, 10.1109/TPAMI.2013.50
Ivanova, 1995, Initialization of neural networks by means of decision trees, Knowl Based Syst, 8, 333, 10.1016/0950-7051(96)81917-4
Sonoda, 2017, Neural network with unbounded activation functions is universal approximator, Appl Comput Harmon Anal, 43, 233, 10.1016/j.acha.2015.12.005
Rockafellar, 1970
Bertsekas, 2015
Schirrmacher, 2017, QuaSI: quantile sparse image prior for spatio-temporal denoising of retinal OCT data, 83
Goodfellow, 2016, vol. 1
Maier, 2009, QMOS – a robust visualization method for speaker dependencies with different microphones, J Pattern Recognit Res, 4, 32, 10.13176/11.112
Schlemper, 2018, Bayesian deep learning for accelerated MR image reconstruction, 64
Dumoulin, 2016
Vincent, 2008, Extracting and composing robust features with denoising autoencoders, 1096
Holden, 2015, Learning motion manifolds with convolutional autoencoders, 18
Vincent, 2010, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, J Mach Learn Res, 11, 3371
Huang, 2007, Unsupervised learning of invariant feature hierarchies with applications to object recognition, 1
Goodfellow, 2016
Arjovsky, 2017, Wasserstein generative adversarial networks, 214
Gauthier, 2014, Conditional generative adversarial nets for convolutional face generation, 2
Zhu, 2017
Szegedy, 2015, Going deeper with convolutions, 1
Ronneberger, 2015, U-Net: convolutional networks for biomedical image segmentation, 234
Çiçek, 2016, 3D U-NET: learning dense volumetric segmentation from sparse annotation, 424
Milletari, 2016, V-Net: fully convolutional neural networks for volumetric medical image segmentation, 565
He, 2015, Deep residual learning for image recognition, 770
Veit, 2016, Residual networks behave like ensembles of relatively shallow networks, 550
Kobler, 2017, Variational networks: connecting variational methods and deep learning, 281
Mandic, 2001
Hochreiter, 1997, Long short-term memory, Neural Comput, 9, 1735, 10.1162/neco.1997.9.8.1735
Chung, 2014
Frid-Adar, 2018
Maier, 2018, Precision learning: towards use of known operators in neural networks, 183
Yuan, 2017
Brown, 2017
Sutton, 1998
Zheng, 2014, Marginal space learning, 25
Ghesu, 2016, Marginal space deep learning: efficient architecture for volumetric image parsing, IEEE Trans Med Imaging, 35, 1217, 10.1109/TMI.2016.2538802
Bier, 2018, X-ray-transform invariant anatomical landmark detection for pelvic trauma surgery, 55
Akselrod-Ballin, 2016, A region based convolutional network for tumor detection and classification in breast mammography, 197
Aubreville, 2017, A guided spatial transformer network for histology cell differentiation, 21
Aubreville, 2018, Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images, Int J Comput Assist Radiol Surg
Ghesu, 2017, Multi-scale deep reinforcement learning for real-time 3D-landmark detection in CT scans, IEEE Trans Pattern Anal Mach Intell, 41, 176, 10.1109/TPAMI.2017.2782687
Breininger, 2018, Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair, Int J Comput Assist Radiol Surg, 13, 10.1007/s11548-018-1779-6
Roth, 2015, DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation, 556
Moeskops, 2016, Automatic segmentation of MR brain images with a convolutional neural network, IEEE Trans Med Imaging, 35, 1252, 10.1109/TMI.2016.2548501
Chen, 2018, Automatic multi-organ segmentation in dual energy CT using 3D fully convolutional network
Nirschl, 2017, Deep learning tissue segmentation in cardiac histopathology images, 179
Middleton, 2004, Segmentation of magnetic resonance images using a combination of neural networks and active contour models, Med Eng Phys, 26, 71, 10.1016/S1350-4533(03)00137-1
Fu, 2018, Frangi-Net: a neural network approach to vessel segmentation, 341
Poudel, 2016, Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation, 83
Andermatt, 2016, Multi-dimensional gated recurrent units for the segmentation of biomedical 3D-data, 142
Wu, 2016, Scalable high-performance image registration framework by unsupervised deep feature representations learning, IEEE Trans Biomed Eng, 63, 1505, 10.1109/TBME.2015.2496253
Schaffert, 2018, Metric-driven learning of correspondence weighting for 2-D/3-D image registration
Miao, 2017, Convolutional neural networks for robust and real-time 2-D/3-D registration, 271
Yang, 2017, Quicksilver: fast predictive image registration – a deep learning approach, NeuroImage, 158, 378, 10.1016/j.neuroimage.2017.07.008
Liao, 2017, An artificial agent for robust image registration, 4168
Krebs, 2017, Robust non-rigid registration through agent-based action learning, 344
Zhong, 2018, Resolve intraoperative brain shift as imitation game
Diamant, 2017, Chest radiograph pathology categorization via transfer learning, 299
De Fauw, 2018, Clinically applicable deep learning for diagnosis and referral in retinal disease, Nat Med, 24, 1342, 10.1038/s41591-018-0107-6
Aubreville, 2017, Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning, Sci Rep, 7, 41598-017, 10.1038/s41598-017-12320-8
Carneiro, 2017, Deep learning models for classifying mammogram exams containing unregistered multi-view images and segmentation maps of lesions, 321
Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056
Wu, 2015, Galileo: perceiving physical object properties by integrating a physics engine with deep learning, 127
Chu, 2017, Data-driven synthesis of smoke flows with CNN-based feature descriptors, ACM Trans Graph, 36, 69, 10.1145/3072959.3073643
Meister, 2018, Towards fast biomechanical modeling of soft tissue using neural networks
Maier, 2018, Deep scatter estimation (DSE): feasibility of using a deep convolutional neural network for real-time X-ray scatter prediction in cone-beam CT, vol. 10573
Unberath, 2018, DeepDRR – a catalyst for machine learning in fluoroscopy-guided procedures, 98
Horger, 2018, Towards arbitrary noise augmentation – deep learning for sampling from arbitrary probability distributions, 129
Han, 2017, MR-based synthetic CT generation using a deep convolutional neural network method, Med Phys, 44, 1408, 10.1002/mp.12155
Stimpel, 2018, MR to X-ray projection image synthesis, 435
Schiffers, 2018, Synthetic fundus fluorescein angiography using deep neural networks, 234
Cohen, 2018, Distribution matching losses can hallucinate features in medical image translation, 529
Wang, 2018, Image reconstruction is a new frontier of machine learning, IEEE Trans Med Imaging, 37, 1289, 10.1109/TMI.2018.2833635
McCann, 2017
Zhang, 2018, A sparse-view CT reconstruction method based on combination of DenseNet and deconvolution, IEEE Trans Med Imaging, 37, 1407, 10.1109/TMI.2018.2823338
Kofler, 2018, A U-Nets cascade for sparse view computed tomography, 91
Zhu, 2018, Image reconstruction by domain-transform manifold learning, Nature, 555, 487, 10.1038/nature25988
Huang, 2018, Some investigations on robustness of deep learning in limited angle tomography, 145
Ye, 2018, Deep convolutional framelets: a general deep learning framework for inverse problems, SIAM J Imaging Sci, 11, 991, 10.1137/17M1141771
Kang, 2018, Deep convolutional framelet denosing for low-dose CT via wavelet residual network, IEEE Trans Med Imaging, 37, 1358, 10.1109/TMI.2018.2823756
Han, 2018, Framing U-Net via deep convolutional framelets: application to sparse-view CT, IEEE Trans Med Imaging, 37, 1418, 10.1109/TMI.2018.2823768
Hammernik, 2018, Learning a variational network for reconstruction of accelerated mri data, Magn Reson Med, 79, 3055, 10.1002/mrm.26977
Vishnevskiy, 2018, Image reconstruction via variational network for real-time hand-held sound-speed imaging, 120
Adler, 2018, Learned primal-dual reconstruction, IEEE Trans Med Imaging, 37, 1322, 10.1109/TMI.2018.2799231
Würfl, 2016, Deep learning computed tomography, 432
Würfl, 2018, Deep learning computed tomography: learning projection-domain weights from image domain in limited angle problems, IEEE Trans Med Imaging, 37, 1454, 10.1109/TMI.2018.2833499
Syben, 2018, Precision learning: Reconstruction filter kernel discretization, 386
Hammernik, 2017, A deep learning architecture for limited-angle computed tomography reconstruction, 92
Syben, 2018, Deriving neural network architectures using precision learning: parallel-to-fan beam conversion
Zhang, 2018, Convolutional neural network based metal artifact reduction in X-ray computed tomography, IEEE Trans Med Imaging, 37, 1370, 10.1109/TMI.2018.2823083
Bier, 2018, Detecting anatomical landmarks for motion estimation in weight-bearing imaging of knees, 83
Li, 2018, Differentiable programming for image processing and deep learning in halide, ACM Trans Graph, 37, 139, 10.1145/3197517.3201383
Wang, 2016, A perspective on deep imaging, IEEE Access, 4, 8914, 10.1109/ACCESS.2016.2624938
Sun, 2017, 1
Oquab, 2017, Is object localization for free? Weakly-supervised learning with convolutional neural networks, 685