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Research Reports Using Deep Learning in Medical Imaging
- Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision. Wen H, Shi J, Zhang Y, Lu KH, Cao J, Liu Z. Cereb Cortex. 2017 Oct 20:1-25. doi: 10.1093/cercor/bhx268
- Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls. Yoo Y, Tang LYW, Brosch T, Li DKB, Kolind S, Vavasour I, Rauscher A, MacKay AL, Traboulsee A, Tam RC. Neuroimage Clin. 2017 Oct 14;17:169-178. doi: 10.1016/j.nicl.2017.10.015. eCollection 2018.
- A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction. Schlemper J, Caballero J, Hajnal JV, Price A, Rueckert D. IEEE Trans Med Imaging. 2017 Oct 13. doi: 10.1109/TMI.2017.2760978. [Epub ahead of print]
- A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery. Liu Y, Stojadinovic S, Hrycushko B, Wardak Z, Lau S, Lu W, Yan Y, Jiang SB, Zhen X, Timmerman R, Nedzi L, Gu X. PLoS One. 2017 Oct 6;12(10):e0185844. doi: 10.1371/journal.pone.0185844. eCollection 2017.
- Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks. Zhang J, Liu M, Shen D. IEEE Trans Image Process. 2017 Oct;26(10):4753-4764. doi: 10.1109/TIP.2017.2721106. Epub 2017 Jun 28.
- Identification of autism spectrum disorder using deep learning and the ABIDE dataset. Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F. Neuroimage Clin. 2017 Aug 30;17:16-23. doi: 10.1016/j.nicl.2017.08.017. eCollection 2018.
- Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys. Kline TL, Korfiatis P, Edwards ME, Blais JD, Czerwiec FS, Harris PC, King BF, Torres VE, Erickson BJ. J Digit Imaging. 2017 Aug;30(4):442-448. doi: 10.1007/s10278-017-9978-1.
- Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR. Trebeschi S, van Griethuysen JJM, Lambregts DMJ, Lahaye MJ, Parmer C, Bakers FCH, Peters NHGM, Beets-Tan RGH, Aerts HJWL. Sci Rep. 2017 Jul 13;7(1):5301. doi: 10.1038/s41598-017-05728-9.
- Automatic Recognition of fMRI-derived Functional Networks using 3D Convolutional Neural Networks. Zhao Y, Dong Q, Zhang S, Zhang W, Chen H, Jiang X, Guo L, Hu X, Han J, Liu T. IEEE Trans Biomed Eng. 2017 Jun 15. doi: 10.1109/TBME.2017.2715281
- Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin. Ghafoorian M, Karssemeijer N, Heskes T, Bergkamp M, Wissink J, Obels J, Keizer K, Leeuw FE, Ginneken BV, Marchiori E, Platel B. Neuroimage Clin. 2017 Feb 4;14:391-399. doi: 10.1016/j.nicl.2017.01.033. eCollection 2017.
- Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework. Yin XX, Zhang Y, Cao J, Wu JL, Hadjiloucas S. Comput Methods Programs Biomed. 2016 Dec;137:87-114. doi: 10.1016/j.cmpb.2016.08.026. Epub 2016 Sep 13
- Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning. Korfiatis P, Kline TL, Erickson BJ.
- Tomography. 2016 Dec;2(4):334-340. doi: 10.18383/j.tom.2016.00166.
- 3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients. Nie D, Zhang H, Adeli E, Liu L, Shen D. Med Image Comput Comput Assist Interv. 2016 Oct;9901:212-220. doi: 10.1007/978-3-319-46723-8_25. Epub 2016 Oct 2.
- Improved spatial accuracy of functional maps in the rat olfactory bulb using supervised machine learning approach. Murphy MC, Poplawsky AJ, Vazquez AL, Chan KC, Kim SG, Fukuda M. Neuroimage. 2016 Aug 15;137:1-8. doi: 10.1016/j.neuroimage.2016.05.055. Epub 2016 May 25.
- Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning. Wu G, Kim M, Wang Q, Munsell BC, Shen D. IEEE Trans Biomed Eng. 2016 Jul;63(7):1505-16. doi: 10.1109/TBME.2015.2496253. Epub 2015 Nov 2. Erratum in: IEEE Trans Biomed Eng. 2017 Jan;64(1):250.
- Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis. Suk HI, Lee SW, Shen D; Alzheimer’s Disease Neuroimaging Initiative. Brain Struct Funct. 2016 Jun;221(5):2569-87. doi: 10.1007/s00429-015-1059-y. Epub 2015 May 21.
- q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans. Golkov V, Dosovitskiy A, Sperl JI, Menzel MI, Czisch M, Samann P, Brox T, Cremers D. IEEE Trans Med Imaging. 2016 May;35(5):1344-1351. doi: 10.1109/TMI.2016.2551324. Epub 2016 Apr 6.
- State-space model with deep learning for functional dynamics estimation in resting-state fMRI. Suk HI, Wee CY, Lee SW, Shen D. Neuroimage. 2016 Apr 1;129:292-307. doi: 10.1016/j.neuroimage.2016.01.005. Epub 2016 Jan 14.
- Supervised learning technique for the automated identification of white matter hyperintensities in traumatic brain injury. Stone JR, Wilde EA, Taylor BA, Tate DF, Levin H, Bigler ED, Scheibel RS, Newsome MR, Mayer AR, Abildskov T, Black GM, Lennon MJ, York GE, Agarwal R, DeVillasante J, Ritter JL, Walker PB, Ahlers ST, Tustison NJ. Brain Inj. 2016;30(12):1458-1468.
- Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. Kim J, Calhoun VD, Shim E, Lee JH. Neuroimage. 2016 Jan 1;124(Pt A):127-146. doi: 10.1016/j.neuroimage.2015.05.018. Epub 2015 May 15.
- Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment. Ithapu VK, Singh V, Okonkwo OC, Chappell RJ, Dowling NM, Johnson SC; Alzheimer's Disease Neuroimaging Initiative. Alzheimers Dement. 2015 Dec;11(12):1489-1499. doi: 10.1016/j.jalz.2015.01.010. Epub 2015 Jun 18.
- A Robust Deep Model for Improved Classification of AD/MCI Patients. Li F, Tran L, Thung KH, Ji S, Shen D, Li J. IEEE J Biomed Health Inform. 2015 Sep;19(5):1610-6. doi: 10.1109/JBHI.2015.2429556. Epub 2015 May 4.
- Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. Zhang W, Li R, Deng H, Wang L, Lin W, Ji S, Shen D. Neuroimage. 2015 Mar;108:214-24. doi: 10.1016/j.neuroimage.2014.12.061. Epub 2015 Jan 3.
- Deep supervised, but not unsupervised, models may explain IT cortical representation. Khaligh-Razavi SM, Kriegeskorte N. PLoS Comput Biol. 2014 Nov 6;10(11):e1003915. doi: 10.1371/journal.pcbi.1003915. eCollection 2014 Nov.
- Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Suk HI, Lee SW, Shen D; Alzheimer's Disease Neuroimaging Initiative. Neuroimage. 2014 Nov 1;101:569-82. doi: 10.1016/j.neuroimage.2014.06.077. Epub 2014 Jul 18.
- Segmenting hippocampus from infant brains by sparse patch matching with deep-learned features. Guo Y, Wu G, Commander LA, Szary S, Jewells V, Lin W, Shent D. Med Image Comput Comput Assist Interv. 2014;17(Pt 2):308-15.
- Deep learning for neuroimaging: a validation study. Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, Johnson HJ, Paulsen JS, Turner JA, Calhoun VD. Front Neurosci. 2014 Aug 20;8:229. doi: 10.3389/fnins.2014.00229. eCollection 2014.
- Deep learning based imaging data completion for improved brain disease diagnosis. Li R, Zhang W, Suk HI, Wang L, Li J, Shen D, Ji S. Med Image Comput Comput Assist Interv. 2014;17(Pt 3):305-12.
- Unsupervised deep feature learning for deformable registration of MR brain images. Wu G, Kim M, Wang Q, Gao Y, Liao S, Shen D. Med Image Comput Comput Assist Interv. 2013;16(Pt 2):649-56.
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