An overview of deep learning in medical imaging focusing on MRI

In the paper

“They are, however, not limited to image processing and analysis but are outperforming other approaches in areas like natural language processing [3, 4, 5], speech recognition and synthesis [6, 7] 2 , and in the analysis of unstructured, tabular-type data using entity embeddings [8, 9].”

What the sources say

• Supported

  This source is cited for the natural-language-processing portion of the sentence, not for speech or tabular data. In §4 Evaluation (page unavailable), the paper reports that adding its deep contextualized representations established a new state-of-the-art result on each of six benchmark NLP tasks. (LLM, conf=0.90)

  cites [3] [3] M. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, L. Zettlemoyer, Deep contextualized word representations, in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), volume 1, pp. 2227-2237. - source
• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [4] [4] J. Howard, S. Ruder, Universal language model finetuning for text classification, in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pp. 328-339. -
• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [5] [5] A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, Improving language understanding by generative pre-training (2018). -
• Partly supported

  The Introduction (p. 1) attributes substantial gains in conversational speech recognition to deep networks, and the Conclusions (p. 6) report a further reduction in word error rate. This citation is attached to “speech recognition and synthesis”; our copy supports recognition but does not address speech synthesis. The separate citations for natural language processing and tabular data are outside this verdict. (LLM, conf=0.88)

  cites [6] [6] W. Xiong, L. Wu, F. Alleva, J. Droppo, X. Huang, A. Stolcke, The Microsoft 2017 Conversational speech recognition system, in: Proc. Speech and Signal Processing (ICASSP) 2018 IEEE Int. Conf. Acoustics, pp. 5934-5938. [54 http://p4mi.org](http://p4mi.org/) - sourceExperimental — accuracy not yet measured
• Partly supported

  The Conclusion (p. 8) says WaveNet outperformed the best existing text-to-speech systems in subjective naturalness. The Speech Recognition section (p. 8) reports the best TIMIT score, to the authors’ knowledge, specifically among models trained directly on raw audio—not superiority over speech-recognition approaches generally. The NLP and tabular-data portions of the citing sentence have separate citations and are not attributed to this paper. (LLM, conf=0.86)

  cites [7] [7] A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, K. Kavukcuoglu, WaveNet: A generative model for raw audio, arXiv preprint arXiv:1609.03499v2 (2016). - sourceExperimental — accuracy not yet measured
• Not checked yetcites [8] [8] C. Guo, F. Berkhahn, Entity embeddings of categorical variables, arXiv preprint arXiv:1604.06737 (2016). - source
• Not checked yetcites [9] [9] A. De Br´ ebisson, ´ E. Simon, A. Auvolat, P. Vincent, Y. Bengio, Artificial neural networks applied to taxi destination prediction, arXiv preprint arXiv:1508.00021 (2015). - source

In the paper

“Convolutional neural networks can be used for efficiency improvement in radiology practices through protocol determination based on short-text classification [87].”

What the sources say

• Not checked yetcites [87] [87] Y. H. Lee, Efficiency improvement in a busy radiology practice: Determination of musculoskeletal magnetic resonance imaging protocol using deeplearning convolutional neural networks., Journal of digital imaging (2018). - source

In the paper

“This change is thanks to the growth of big data , powerful processors for parallel computations (in particular, GPUs), some important tweaks to the algorithms used to construct and train the networks, and the development of easy-to-use software frameworks.”

In the paper

“Most famously on the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) 1 in 2012 [1] when a deep learning model (a convolutional neural network ) halved the second best error rate on the image classification task.”

What the sources say

• Not checked yetcites [1] [1] A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet classification with deep convolutional neural networks, in: F. Pereira, C. J. C. Burges, L. Bottou, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25, Curran Associates, Inc., 2012, pp. 1097-1105. -

In the paper

“Deep learning rose to its prominent position in computer vision when neural networks started outperforming other methods on several high-profile image analysis benchmarks.”

What the sources say

• Not checked yetcites [1] [1] A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet classification with deep convolutional neural networks, in: F. Pereira, C. J. C. Burges, L. Bottou, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25, Curran Associates, Inc., 2012, pp. 1097-1105. -

In the paper

“Artificial neural networks (ANNs) is one of the most famous machine learning models, introduced already in the 1950s, and actively studied since [41, Chapter 1.2].”

What the sources say

• Not checked yetcites [41, Chapter 1.2]

In the paper

“In deep learning, the computers learn useful representations and features automatically, directly from the raw data, bypassing this manual and difficult step.”

In the paper

“Interestingly, CNNs was applied in medical image analysis already in the early 90s, e.g. [58], but with limited success.”

What the sources say

• Not checked yetcites [58] [58] S. C. Lo, M. T. Freedman, J. S. Lin, S. K. Mun, Automatic lung nodule detection using profile matching and back-propagation neural network techniques., Journal of digital imaging 6 (1993) 4854. -

In the paper

“Healthcare applications of deep learning range from one-dimensional biosignal analysis [17] and the prediction of medical events, e.g. seizures [18] and cardiac arrests [19], to computer-aided detection [20] and diagnosis [21] supporting clinical decision making and survival analysis [22], to drug discovery [23] and as an aid in therapy selection and pharmacogenomics [24], to increased operational”

What the sources say

• Not checked yetcites [17] [17] N. Ganapathy, R. Swaminathan, T. M. Deserno, Deep learning on 1-D biosignals: a taxonomy-based survey, Yearbook of medical informatics 27 (2018) 98-109. - source
• Not checked yetcites [20] [20] H.-C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, R. M. Summers, Deep convolutional neural networks for computeraided detection: Cnn architectures, dataset characteristics and transfer learning., IEEE transactions on medical imaging 35 (2016) 1285-1298. - source
• ?Not matched

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  cites [23] [23] J. Jim´ enez, M. ˇ Skaliˇ c, G. Mart´ ınez-Rosell, G. De Fabritiis, KDEEP: Protein-Ligand absolute binding affinity prediction via 3D-Convolutional Neural Networks, Journal of Chemical Information and Modeling 58 (2018) 287-296. -

In the paper

“In medical imaging the interest in deep learning is mostly triggered by convolutional neural networks (CNNs) [57] 15 , a powerful way to learn useful representations of images and other structured data.”

What the sources say

• Not checked yetcites [57] [57] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86 (1998) 22782324. - source

In the paper

“Deep learning is applied in radiotherapy [89], in PET-MRI attenuation correction [90, 91], in radiomics [92, 93]”

What the sources say

• Not checked yetcites [90] [90] F. Liu, H. Jang, R. Kijowski, T. Bradshaw, A. B. McMillan, Deep learning MR imaging-based attenuation correction for PET/MR imaging, Radiology 286 (2018) 676-684. - source
• Not checked yetcites [91] [91] A. Mehranian, H. Arabi, H. Zaidi, Vision 20/20: Magnetic resonance imaging-guided attenuation correction in pet/mri: Challenges, solutions, and opportunities., Medical physics 43 (2016) 11301155. - source

In the paper

“there are several strong initiatives across academia, and multiple large companies are pursuing healthcare projects based on machine learning.”

What the sources say

• Not checked yetcites [29] [29] V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, et al., Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, Jama 316 (2016) 2402-2410. - source
• Not checked yetcites [30] [30] R. Poplin, A. V. Varadarajan, K. Blumer, Y. Liu, M. McConnell, G. Corrado, L. Peng, D. Webster, Predicting Cardiovascular Risk Factors in Retinal Fundus Photographs using Deep Learning, Nature Biomedical Engineering (2018). - source
• Not checked yetcites [31] [31] R. Poplin, P.-C. Chang, D. Alexander, S. Schwartz, T. Colthurst, A. Ku, D. Newburger, J. Dijamco, N. Nguyen, P. T. Afshar, S. S. Gross, L. Dorfman, C. Y. McLean, M. A. DePristo, A universal SNP and small-indel variant caller using deep neural networks, Nature Biotechnology (2018). - source
• Not checked yetcites [32] [32] J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O'Donoghue, D. Visentin, et al., Clinically applicable deep learning for diagnosis and referral in retinal disease, Nature medicine 24 (2018) 1342. - source
• Not checked yetcites [33] [33] Y. Qin, K. Kamnitsas, S. Ancha, J. Nanavati, G. Cottrell, A. Criminisi, A. Nori, Autofocus Layer for Semantic Segmentation, arXiv preprint arXiv:1805.08403 (2018). - source
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  cites [34] [34] K. Kamnitsas, C. Baumgartner, C. Ledig, V. Newcombe, J. Simpson, A. Kane, D. Menon, A. Nori, A. Criminisi, D. Rueckert, et al., Unsupervised domain adaptation in brain lesion segmentation with adversarial networks, in: International Conference on Information Processing in Medical Imaging, Springer, pp. 597-609. -

In the paper

“Removing the pooling layers simplifies the network architecture without necessarily sacrificing performance [62].”

What the sources say

• Not checked yetcites [62] [62] J. T. Springenberg, A. Dosovitskiy, T. Brox, M. Riedmiller, Striving for simplicity: The all convolutional net, arXiv preprint arXiv:1412.6806 (2014). -

In the paper

“A CNN architecture designed by a neural network, beating all the previous human-designed networks at the ILSVRC competition.”

What the sources say

• Not checked yetcites [77] [77] B. Zoph, V. Vasudevan, J. Shlens, Q. V. Le, Learning transferable architectures for scalable image recognition, arXiv preprint arXiv:1707.07012 2 (2017). -

In the paper

“This makes it possible for the entire neural network to approximate almost any nonlinear function [48, 49] 19”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [48] [48] M. Leshno, V. Y. Lin, A. Pinkus, S. Schocken, Multilayer feedforward networks with a nonpolynomial activation function can approximate any function, Neural networks 6 (1993) 861-867. -
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  cites [49] [49] S. Sonoda, N. Murata, Neural network with unbounded activation functions is universal approximator, Applied and Computational Harmonic Analysis 43 (2017) 233-268. -

In the paper

“In the early days of modern deep learning one tended to use very simple combinations of the building blocks, as in Lenet [57] and AlexNet [1].”

What the sources say

• Not checked yetcites [57] [57] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86 (1998) 22782324. - source
• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [1] [1] A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet classification with deep convolutional neural networks, in: F. Pereira, C. J. C. Burges, L. Bottou, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25, Curran Associates, Inc., 2012, pp. 1097-1105. -

In the paper

“A 152 layer deep ResNet won the 2015 ILSVRC competition, and the authors also successfully trained a version with 1001 layers.”

What the sources say

• Not checked yetcites [72] [72] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. -

In the paper

“Deep learning is applied in radiotherapy [89], in PET-MRI attenuation correction [90, 91], in radiomics [92, 93]”

What the sources say

• Not checked yetcites [92] [92] J. Lao, Y. Chen, Z.-C. Li, Q. Li, J. Zhang, J. Liu, G. Zhai, A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme., Scientific reports 7 (2017) 10353. - source
• Not checked yetcites [93] [93] L. Oakden-Rayner, G. Carneiro, T. Bessen, J. C. Nascimento, A. P. Bradley, L. J. Palmer, Precision radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework., Scientific reports 7 (2017) 1648. - source

In the paper

“The network that launched the current deep learning boom by winning the 2012 ILSVRC competition by a huge margin.”

What the sources say

• Not checked yetcites [1] [1] A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet classification with deep convolutional neural networks, in: F. Pereira, C. J. C. Burges, L. Bottou, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25, Curran Associates, Inc., 2012, pp. 1097-1105. -

In the paper

“Deep learning techniques have become the de facto standard for a wide variety of computer vision problems.”

In the paper

“But by now, artificial neural networks form one of the dominant methods in machine learning, and the most intensively studied.”

In the paper

“Once it became possible to use features learned directly from the data, many of the handcrafted image features were typically left by the wayside as they turned out to be almost worthless compared to feature detectors found by CNNs.”

In the paper

“Deep learning methods are increasingly used to improve clinical practice, and the list of examples is long, growing daily.”

In the paper

“Including batch normalization layers forces the network to periodically change its activations to zero mean and unit standard deviation as the training batch hits these layers, which works as a regularizer for the network, speeds up training, and makes it less dependent on careful parameter initialization [67].”

What the sources say

• Not checked yetcites [67] [67] S. Ioffe, C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, in: International Conference on Machine Learning, pp. 448-456. -

In the paper

“Introduced skip connections , which makes it possible to train much deeper networks.”

What the sources say

• Not checked yetcites [72] [72] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. -

In the paper

“These can also be used for biological image synthesis [208, 209] and text-to-image synthesis [210, 211, 212].”

What the sources say

• Not checked yetcites [208] [208] A. Osokin, A. Chessel, R. E. C. Salas, F. Vaggi, Gans for biological image synthesis, in: Proc. IEEE Int. Conf. Computer Vision (ICCV), pp. 2252-2261. -
• Not checked yetcites [209] [209] G. Antipov, M. Baccouche, J. Dugelay, Face aging with conditional generative adversarial networks, in: Proc. IEEE Int. Conf. Image Processing (ICIP), pp. 2089-2093. - source
• Not checked yetcites [210] [210] C. Bodnar, Text to image synthesis using generative adversarial networks, arXiv preprent arXiv:1805.00676v1 (2018). - source
• Not checked yetcites [211] [211] H. Dong, S. Yu, C. Wu, Y. Guo, Semantic image synthesis via adversarial learning, arXiv preprint arXiv:1707.06873v1 (2017). -
• Not checked yetcites [212] [212] S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, H. Lee, Generative adversarial text to image synthesis, arXiv preprint arXiv:1605.05396v2 (2016). -

In the paper

“convolutional recurrent neural networks for dynamic MR image reconstruction [135], reconstructing good quality cardiac MR images from highly undersampled complex-valued k -space data by learning spatio-temporal dependencies, outperforming 3D CNN approaches and compressed sensing-based dynamic MRI reconstruction algorithms in computational complexity, reconstruction accuracy and speed for different”

What the sources say

• Not checked yetcites [135] [135] C. Qin, J. V. Hajnal, D. Rueckert, J. Schlemper, J. Caballero, A. N. Price, Convolutional recurrent neural networks for dynamic mr image reconstruction., IEEE transactions on medical imaging (2018). - source

In the paper

“A unified framework for image reconstruction [140], called automated transform by manifold approximation (AUTOMAP) consisting of a feedforward deep neural network with fully connected layers followed by a sparse convolutional autoencoder, formulate image reconstruction generically as a data-driven supervised learning task that generates a mapping between the sensor and the image domain based on an”

What the sources say

• Not checked yetcites [140] [140] B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, M. S. Rosen, Image reconstruction by domain-transform manifold learning., Nature 555 (2018) 487-492. - source

In the paper

“The last couple of years have seen impressive results for photo-realistic image synthesis using deep learning techniques, especially generative adversarial networks (GANs, introduced by Goodfellow et al. in 2014 [80]), e.g. [205, 206, 207].”

What the sources say

• Not checked yetcites [80] [80] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative Adversarial Nets, in: Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27, Curran Associates, Inc., 2014, pp. 2672-2680. -
• Not checked yetcites [205] [205] A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, A. A. Bharath, Generative adversarial networks: An overview, IEEE Signal Processing Magazine 35 (2018) 53-65. -
• Not checked yetcites [206] [206] Y. Hong, U. Hwang, J. Yoo, S. Yoon, How generative adversarial networks and their variants work: An overview of GAN, arXiv preprint arXiv:1711.05914v7 (2017). -
• Not checked yetcites [207] [207] H. Huang, P. S. Yu, C. Wang, An introduction to image synthesis with generative adversarial nets, arXiv preprint arXiv:1803.04469v1 (2018). -

In the paper

“Super-resolution for MRI have been around for almost 10 years [192, 193] and can be used to improve the trade-off between resolution, SNR, and acquisition time [194], generate 7T-like MR images on 3T MRI scanners [195], or obtain super-resolution T 1 maps from a set of low resolution T 1 weighted images [196].”

What the sources say

• Not checked yetcites [194] [194] E. Plenge, D. H. J. Poot, M. Bernsen, G. Kotek, G. Houston, P. Wielopolski, L. van der Weerd, W. J. Niessen, E. Meijering, Super-resolution methods in mri: can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time?, Magnetic resonance in medicine 68 (2012) 19831993. - source
• Not checked yetcites [195] [195] K. Bahrami, F. Shi, I. Rekik, Y. Gao, D. Shen, 7Tguided super-resolution of 3T MRI, Medical physics 44 (2017) 1661-1677. - source
• Not checked yetcites [196] [196] G. Van Steenkiste, D. H. J. Poot, B. Jeurissen, A. J. den Dekker, F. Vanhevel, P. M. Parizel, J. Sijbers, Super-resolution t, javax.xml.bind.jaxbelement@1458c115, estimation: Quantitative high resolution t, javax.xml.bind.jaxbelement@62365375, mapping from a set of low resolution t, javax.xml.bind.jaxbelement@20656587, -weighted images with different slice orientations., Magnetic resonance in medicine 77 (2017) 1818-1830. - source

In the paper

“Such generative models for image synthesis serve as a form of data augmentation, and also as an anonymization tool.”

In the paper

“Research on CNN and RNN-based image reconstruction methods is rapidly increasing, pioneered by Yang et al. [133] at NIPS 2016 and Wang et al. [134] at ISBI 2016.”

What the sources say

• Not checked yetcites [133] [133] Y. Yang, J. Sun, H. Li, Z. Xu, Deep ADMMNet for compressive sensing MRI, in: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, R. Garnett (Eds.), Advances in Neural Information Processing Systems 29, Curran Associates, Inc., 2016, pp. 1018. -
• Not checked yetcites [134] [134] S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, D. Liang, Accelerating magnetic resonance imaging via deep learning, in: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, IEEE, pp. 514-517. -

In the paper

“and for theranostics in neurosurgical imaging, combining confocal laser endomicroscopy with deep learning models for automatic detection of intraoperative CLE images on-the-fly [95].”

What the sources say

• Not checked yetcites [95] [95] M. Izadyyazdanabadi, E. Belykh, M. A. Mooney, J. M. Eschbacher, P. Nakaji, Y. Yang, M. C. Preul, Prospects for theranostics in neurosurgical imaging: Empowering confocal laser endomicroscopy diagnostics via deep learning., Frontiers in oncology 8 (2018) 240. - source

In the paper

“Quantitative susceptibility mapping (QSM) is a growing field of research in MRI, aiming to noninvasively estimate the magnetic susceptibility of biological tissue [145, 146].”

What the sources say

• Not checked yetcites [145] [145] A. Deistung, A. Sch¨ afer, F. Schweser, U. Biedermann, R. Turner, J. R. Reichenbach, Toward in vivo histology: a comparison of quantitative susceptibility mapping (QSM) with magnitude-, phase-, and R2*-imaging at ultra-high magnetic field strength, Neuroimage 65 (2013) 299-314. - source
• Not checked yetcites [146] [146] A. Deistung, F. Schweser, J. R. Reichenbach, Overview of quantitative susceptibility mapping, NMR in Biomedicine 30 (2017). - source

In the paper

“Matching one time series was about 100 times faster than the conventional [150] matching method and with very small mean absolute deviations from ground truth values.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [166] [166] E. Hoppe, G. Krzdrfer, T. Wrfl, J. Wetzl, F. Lugauer, J. Pfeuffer, A. Maier, Deep learning for magnetic resonance fingerprinting: A new approach for predicting quantitative parameter values from time series, Studies in health technology and informatics 243 (2017) 202-206. -

In the paper

“After training their model on synthetic data it was able to generalize to real-world clinical brain MRI data, computing susceptibility maps within seconds end-to-end.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [149] [149] K. G. B. Rasmussen, M. J. Kristensen, R. G. Blendal, L. R. Ostergaard, M. Plocharski, K. O'Brien, C. Langkammer, A. Janke, M. Barth, S. Bollmann, DeepQSM-Using Deep Learning to Solve the Dipole Inversion for MRI Susceptibility Mapping, Biorxiv (2018) 278036. -

In the paper

“Schlemper et.al. [136] created a deep cascade of concatenated CNNs for dynamic MR image reconstruction, making use of data augmentation, both rigid and elastic deformations, to increase the variation of the examples seen by the network and reduce overfitting 28 .”

What the sources say

• Not checked yetcites [136] [136] J. Schlemper, J. Caballero, J. V. Hajnal, A. N. Price, D. Rueckert, A deep cascade of convolutional neural networks for dynamic MR image reconstruction., IEEE transactions on medical imaging 37 (2018) 491-503. -

In the paper

“In this context, deep learning approaches have accelerated research and the amount of costly training data.”

In the paper

“Recently, deep learning approaches have been introduced to denoising.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [180] [180] C. Bermudez, A. J. Plassard, T. L. Davis, A. T. Newton, S. M. Resnick, B. A. Landman, Learning implicit brain MRI manifolds with deep learning, Proceedings of SPIE-the International Society for Optical Engineering 10574 (2018). -
• Not checked yetcites [181] [181] A. Benou, R. Veksler, A. Friedman, T. Riklin Raviv, Ensemble of expert deep neural networks for spatiotemporal denoising of contrast-enhanced MRI sequences, Medical image analysis 42 (2017) 145-159. - source

In the paper

“Deep learning in MRI has typically been focused on segmentation and classification of reconstructed magnitude images.”

In the paper

“It uses a pseudo-randomized acquisition that causes the signals from different tissues to have a unique signal evolution ('fingerprint') that is a function of the multiple material properties being investigated.”

What the sources say

• Not checked yetcites [150] [150] D. Ma, V. Gulani, N. Seiberlich, K. Liu, J. L. Sunshine, J. L. Duerk, M. A. Griswold, Magnetic resonance fingerprinting, Nature 495 (2013) 187-192. - source

In the paper

“Deep learning methods has also been applied to MR artifact detection , e.g. poor quality spectra in MRSI [188]; detection and removal of ghosting artifacts in MR spectroscopy [189]; and automated reference-free detection of patient motion artifacts in MRI [190].”

What the sources say

• Not checked yetcites [188] [188] S. S. Gurbani, E. Schreibmann, A. A. Maudsley, J. S. Cordova, B. J. Soher, H. Poptani, G. Verma, P. B. Barker, H. Shim, L. A. D. Cooper, A convolutional neural network to filter artifacts in spectroscopic MRI, Magnetic resonance in medicine 80 (2018) 1765-1775. - source
• Not checked yetcites [189] [189] S. P. Kyathanahally, A. Dring, R. Kreis, Deep learning approaches for detection and removal of ghosting artifacts in MR spectroscopy, Magnetic resonance in medicine 80 (2018) 851-863. - source
• ?Not matched

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  cites [190] [190] T. K¨ ustner, A. Liebgott, L. Mauch, P. Martirosian, F. Bamberg, K. Nikolaou, B. Yang, F. Schick, S. Gatidis, Automated reference-free detection of motion artifacts in magnetic resonance images, MAGMA 31 (2018) 243-256. -

In the paper

“Using variational networks for single-shot fast spin-echo MRI with variable density sampling, Chen et.al. [137] enabled real-time (200 ms per section) image reconstruction, outperforming conventional parallel imaging and compressed sensing reconstruction.”

What the sources say

• Not checked yetcites [137] [137] F. Chen, V. Taviani, I. Malkiel, J. Y. Cheng, J. I. Tamir, J. Shaikh, S. T. Chang, C. J. Hardy, J. M. Pauly, S. S. Vasanawala, Variable-density singleshot fast Spin-Echo MRI with deep learning reconstruction by using variational networks, Radiology (2018) 180445. -

In the paper

“It has an almost 50 years long history, and has become the biggest target for deep learning approaches in medical imaging.”

In the paper

“It can be regarded as a quantitative multiparametric MRI analysis, and with recent acquisition schemes using a single-shot spiral trajectory with undersampling, whole-brain coverage of T 1 , T 2 and proton density maps can be acquired at 1 . 2 × 1 . 2 × 3 mm 3 voxel resolution in less than 5 min [159].”

What the sources say

• Not checked yetcites [159] [159] D. Ma, Y. Jiang, Y. Chen, D. McGivney, B. Mehta, V. Gulani, M. Griswold, Fast 3D magnetic resonance fingerprinting for a whole-brain coverage, Magnetic resonance in medicine 79 (2018) 21902197. - source

In the paper

“It is also possible to add attention mechanisms to neural networks [346, 347], enabling them to focus their resources on the most informative components of each layer input.”

What the sources say

• Not checked yetcites [346] [346] V. Mnih, N. Heess, A. Graves, et al., Recurrent models of visual attention, in: Advances in neural information processing systems, pp. 2204-2212. -
• Not checked yetcites [347] [347] K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, Y. Bengio, Show, attend and tell: Neural image caption generation with visual attention, in: International conference on machine learning, pp. 2048-2057. -

In the paper

“These are very inefficient models, requiring large number of training samples before they can produce anything remotely useful”

In the paper

“Recently, deep learning methods have also been used for automated generation of radiology reports , typically incorporating long-short-term-memory (LSTM) network models to generate the textual paragraphs [314, 315, 316, 317], and also to identify findings in radiology reports [318, 319, 320].”

What the sources say

• Not checked yetcites [314] [314] B. Jing, P. Xie, E. Xing, On the automatic generation of medical imaging reports, arXiv preprint arXiv:1711.08195v3 (2017). - source
• Not checked yetcites [315] [315] C. Y. Li, X. Liang, Z. Hu, E. P. Xing, Hybrid retrieval-generation reinforced agent for medical image report generation, arXiv preprint arXiv:1805.08298v1 (2018). - source
• Not checked yetcites [316] [316] M. Moradi, A. Madani, Y. Gur, Y. Guo, T. SyedaMahmood, Bimodal network architectures for automatic generation of image annotation from text, arXiv preprint arXiv:1809.01610v1 (2018). - source
• Not checked yetcites [317] [317] Y. Zhang, D. Y. Ding, T. Qian, C. D. Manning, C. P. Langlotz, Learning to summarize radiology findings, arXiv preprint arXiv:1809.04698v1 (????). - source
• Not checked yetcites [318] [318] E. Pons, L. M. M. Braun, M. G. M. Hunink, J. A. Kors, Natural language processing in radiology: A systematic review, Radiology 279 (2016) 329-343. - source
• Not checked yetcites [319] [319] J. Zech, M. Pain, J. Titano, M. Badgeley, J. Schefflein, A. Su, A. Costa, J. Bederson, J. Lehar, E. K. Oermann, Natural language-based machine learning models for the annotation of clinical radiology reports, Radiology 287 (2018) 570-580. - source
• Not checked yetcites [320] [320] D. J. Goff, T. W. Loehfelm, Automated radiology report summarization using an open-source natural language processing pipeline, Journal of digital imaging 31 (2018) 185-192. - source

In the paper

“There are a few examples of these ideas entering the medical machine learning community, as in [340] where the distribution of deep learning models among several medical institutions was investigated, but then without considering the above privacy issues.”

What the sources say

• Not checked yetcites [340] [340] K. Chang, N. Balachandar, C. Lam, D. Yi, J. Brown, A. Beers, B. Rosen, D. L. Rubin, J. Kalpathy-Cramer, Distributed deep learning networks among institutions for medical imaging, Journal of the American Medical Informatics Association : JAMIA 25 (2018) 945-954. -

In the paper

“But still, even if there is interest on the 'ground floor' and one is able to get prototype systems into the hands of clinicians, there are many higher-ups to convince and regulatory, ethical and legal hurdles to overcome.”

In the paper

“However, the networks that are most frequently used, and with the best raw performance, remain the data-hungry standard deep neural networks.”

In the paper

“For two-dimensional images one will almost always use a network that has been pre-trained on the ImageNet data set.”

In the paper

“It is clear that the ideas behind stateof-the-art two-dimensional CNNs can be lifted to three dimensions, but also that adding a third spatial dimension results in additional constraints.”

In the paper

“What's true about the openness of machine learning in general is increasingly true also for the sub-field of machine learning for medical image analysis.”

In the paper

“The field of Bayesian Deep Learning aims to combine deep learning and Bayesian approaches to uncertainty.”

In the paper

“Attempting to augment already established procedures necessitates knowledge of the entire workflow.”

In the paper

“It is possible to end up developing clever machine learning system for clinical use that turn out to be practically useless for actual clinicians.”

In the paper

“The idea of pre-training networks is not restricted to images. Pre-training entire models has recently been demonstrated to greatly impact the performance of natural language processing systems [3, 4, 5].”

What the sources say

• Not checked yetcites [3] [3] M. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, L. Zettlemoyer, Deep contextualized word representations, in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), volume 1, pp. 2227-2237. -
• Not checked yetcites [4] [4] J. Howard, S. Ruder, Universal language model finetuning for text classification, in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pp. 328-339. -
• Not checked yetcites [5] [5] A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, Improving language understanding by generative pre-training (2018). -

In the paper

“Most current work on deep learning for medical data analysis use either open, anonymized data sets (as those in Table 4), or locally obtained anonymized research data, making these issues less relevant.”

In the paper

“One of the first application of deep learning for CBIR in the medical domain came in 2015 when Sklan et al. [310] trained a CNN to perform CBIR with more than one million random MR and CT images, with disappointing results (true positive rate of 20%) on their independent test set of 2100 labeled images.”

What the sources say

• Not checked yetcites [310] [310] J. E. S. Sklan, A. J. Plassard, D. Fabbri, B. A. Landman, Toward content based image retrieval with deep convolutional neural networks, Proceedings of SPIE-the International Society for Optical Engineering 9417 (2015). -

In the paper

“However, the story for deep learning in medical imaging is not quite as settled.”

In the paper

“Another way to increase their trustworthiness is to make them produce robust uncertainty estimates in addition to predictions.”

In the paper

“Using the basic building blocks described above, placed according to the ideas behind, say, ResNet and SENet, will easily result in close to state-of-the-art performance on two-dimensional object detection, image classification and segmentation tasks.”

In the paper

“This is illustrated by the tremendous amounts of high-impact publications in top-journals dealing with deep learning in medical imaging (for example [17, 21, 30, 32, 40, 90, 162, 137, 140, 273, 285], all published in 2018).”

What the sources say

• Not checked yetcites [17] [17] N. Ganapathy, R. Swaminathan, T. M. Deserno, Deep learning on 1-D biosignals: a taxonomy-based survey, Yearbook of medical informatics 27 (2018) 98-109. - source
• Not checked yetcites [21] [21] D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, K. Zhang, Identifying medical diagnoses and treatable diseases by image-based deep learning, Cell 172 (2018) 1122-1131.e9. - source
• Not checked yetcites [30] [30] R. Poplin, A. V. Varadarajan, K. Blumer, Y. Liu, M. McConnell, G. Corrado, L. Peng, D. Webster, Predicting Cardiovascular Risk Factors in Retinal Fundus Photographs using Deep Learning, Nature Biomedical Engineering (2018). - source
• Not checked yetcites [32] [32] J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O'Donoghue, D. Visentin, et al., Clinically applicable deep learning for diagnosis and referral in retinal disease, Nature medicine 24 (2018) 1342. - source
• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [40] [40] G. Hinton, Deep Learning A Technology With the Potential to Transform Health Care (2018) 1-2. -
• Not checked yetcites [90] [90] F. Liu, H. Jang, R. Kijowski, T. Bradshaw, A. B. McMillan, Deep learning MR imaging-based attenuation correction for PET/MR imaging, Radiology 286 (2018) 676-684. - source
• Not checked yetcites [162] [162] B. Rieger, M. Akakaya, J. C. Pariente, S. Llufriu, E. Martinez-Heras, S. Weingrtner, L. R. Schad, Time efficient whole-brain coverage with MR fingerprinting using slice-interleaved echo-planarimaging, Scientific reports 8 (2018) 6667. - source
• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [137] [137] F. Chen, V. Taviani, I. Malkiel, J. Y. Cheng, J. I. Tamir, J. Shaikh, S. T. Chang, C. J. Hardy, J. M. Pauly, S. S. Vasanawala, Variable-density singleshot fast Spin-Echo MRI with deep learning reconstruction by using variational networks, Radiology (2018) 180445. -
• Not checked yetcites [140] [140] B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, M. S. Rosen, Image reconstruction by domain-transform manifold learning., Nature 555 (2018) 487-492. - source
• Not checked yetcites [273] [273] J. Wasserthal, P. Neher, K. H. Maier-Hein, TractSeg - fast and accurate white matter tract segmentation, Neuroimage 183 (2018) 239-253. - source

In the paper

“As privacy and data protection is often a requirement when dealing with medical data, new techniques for training models without exposing the underlying training data to the user of the model are necessary.”

In the paper

“Other important challenges are related to data, trust, interpretability, workflow integration, and regulations, as discussed below.”

In the paper

“As deep neural networks relies on complicated interconnected hierarchical representations of the training data to produce its predictions, interpreting these predictions becomes very difficult. This is the 'black box' problem of deep neural networks [348].”

What the sources say

• Not checked yetcites [348] [348] D. Castelvecchi, Can we open the black box of AI?, Nature News 538 (2016) 20. - source

In the paper

“Even though there are many challenges associated to the introduction of deep learning in clinical settings, the methods produce results that are too valuable to discard.”

In the paper

“The ideas date back to the early 90s [354, 355, 356], but the field has recently seen renewed interest from the machine learning community at large, as new ways of computing uncertainty estimates from state of the art deep learning models have been developed [59, 65, 357].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [354] [354] R. M. Neal, Bayesian learning for neural networks, Ph.D. thesis, University of Toronto, 1995. -
• Not checked yetcites [355] [355] D. J. MacKay, A practical Bayesian framework for backpropagation networks, Neural computation 4 (1992) 448-472. - source
• Not checked yetcites [356] [356] P. Dayan, G. E. Hinton, R. M. Neal, R. S. Zemel, The Helmholtz machine, Neural computation 7 (1995) 889-904. - source
• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [59] [59] S. Murray, An exploratory analysis of multi-class uncertainty approximation in Bayesian convolution neural networks, Master's thesis, University of Bergen, 2018. -
• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [65] [65] Y. Gal, Uncertainty in deep learning, Ph.D. thesis, University of Cambridge, 2016. -
• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [357] [357] Y. Li, Y. Gal, Dropout Inference in Bayesian Neural Networks with Alpha-divergences, in: International Conference on Machine Learning, pp. 2052-2061. -

In the paper

“This is a crucially important obstacle for deep neural networks, especially in medical data analysis.”

In the paper

“It is interesting to note how deep learning-based models are completely dominating the leaderboards of essentially all image-based competitions. Other machine learning models, or non-machine learning-based techniques, have largely been outclassed.”

In the paper

“Another widely used technique is augmenting the training data set by applying various transformations that preserves the labels, as in rotations, scalings and intensity shifts of images, or more advanced data augmentation techniques like anatomically sound deformations, or other data set specific operations (for example in our work on kidney segmentation from DCE-MRI, where we used image”

What the sources say

• Not checked yetcites [343] [343] A. Lundervold, K. Sprawka, A. Lundervold, Fast estimation of kidney volumes and time courses in DCE-MRI using convolutional neural networks, 2018. -

In the paper

“But whatever you do, the way current deep neural networks are constructed and trained results in significant data size requirements.”