Deep Learning in Medical Image Analysis

LITERATURE CITED

1. 
   Brody H. 2013. Medical imaging. Nature 502:S81
   
2. 
   Shao Y, Gao Y, Guo Y, Shi Y, Yang X, Shen D. 2014. Hierarchical lung field segmentation with joint shape and appearance sparse learning. IEEE Trans. Med. Imaging 33:1761–80
   
3. 
   Wang L, Chen KC, Gao Y, Shi F, Liao S, et al. 2014. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization. Med. Phys. 41:043503
   
4. 
   Yap PH, Zhang Y, Shen D. 2016. Multi-tissue decomposition of diffusion MRI signals via L0 sparse- group estimation. IEEE Trans. Image Process. 25:4340–53
   
5. 
   Suk HI, Lee SW, Shen D. 2016. Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis. Brain Struct. Funct. 221:2569–87
   
6. 
   
   Annu. Rev. Biomed. Eng. 2017.19:221-248. Downloaded from www.annualreviews.org Access provided by 82.215.98.77 on 06/08/22. For personal use only.
   Chen Y, Juttukonda M, Su Y, Benzinger T, Rubin BG, et al. 2015. Probabilistic air segmentation and sparse regression estimated pseudo CT for PET/MR attenuation correction. Radiology 275:562–69
   
7. 
   Schmidhuber J. 2015. Deep learning in neural networks: an overview. Neural Netw. 61:85–117
   
8. 
   Bengio Y. 2009. Learning Deep Architectures for AI: Foundations and Trends in Machine Learning. Boston: Now. 127 pp.
   
9. 
   LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
   
10. 
    Hinton GE, Salakhutdinov RR. 2006. Reducing the dimensionality of data with neural networks. Science 313:504–7
    
11. 
    Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA. 2010. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11:3371–408
    
12. 
    Nair V, Hinton GE. 2010. Rectified linear units improve restricted Boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning, pp. 807–14. New York: ACM
    
13. 
    Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. 2014. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15:1929–58
    
14. 
    Ioffe S, Szegedy C. 2015. Batch normalization: accelerating deep network training by reducing inter- nal covariate shift. In Proceedings of the 32nd International Conference on Machine Learning, pp. 448–56. New York: ACM
    
15. 
    Bishop CM. 1995. Neural Networks for Pattern Recognition. Oxford, UK: Oxford Univ. Press
    
16. 
    Collobert R, Weston J. 2008. A unified architecture for natural language processing: deep neural networks with multitask learning. In Proceedings of the 25th International Conference on Machine Learning, pp. 160–67. New York: ACM
    
17. 
    Sutskever I, Martens J, Hinton GE. 2011. Generating text with recurrent neural networks. In Proceedings of the 28th International Conference on Machine Learning, pp. 1017–24. New York: ACM
    
18. 
    Hinton GE, Deng L, Yu D, Dahl GE, Mohamed A, et al. 2012. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Proc. Mag. 29:82– 97
    
19. 
    Szegedy C, Toshev A, Erhan D. 2013. Deep neural networks for object detection. In Proceedings of the 26th Neural Information Processing Systems Conference (NIPS 2013), ed. CJC Burges, L Bottou, M Welling, Z Ghahramani, KQ Weinberger, pp. 2553–61. https://papers.nips.cc/paper/5207-deep- neural-networks-for-object-detection
    
20. 
    Taigman Y, Yang M, Ranzato M, Wolf L. 2014. DeepFace: closing the gap to human-level performance in face verification. In Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–8. Washington, DC: IEEE
    
21. 
    Zhang J, Zong C. 2015. Deep neural networks in machine translation: an overview. IEEE Intell. Syst.
    

30:16–25

22. 
    Karpathy A, Li F. 2015. Deep visual–semantic alignments for generating image descriptions. In Proceed- ings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3128–37. Washington, DC: IEEE
    
23. 
    Silver D, Huang A, Maddison CJ, Guez A, Sifre L, et al. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529:484–89
    
24. 
    Russakovsky O, Deng J, Su H, Krause J, Satheesh S, et al. 2015. ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115:211–52
    
25. 
    Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A. 2012. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) results. http://host.robots.ox.ac.uk/pascal/VOC/voc2012/
    
26. 
    Zhang W, Li R, Deng H, Wang L, Lin W, et al. 2015. Deep convolutional neural networks for multi- modality isointense infant brain image segmentation. NeuroImage 108:214–24
    
27. 
    Kleesiek J, Urban G, Hubert A, Schwarz D, Maier-Hein K, et al. 2016. Deep MRI brain extraction: a 3D convolutional neural network for skull stripping. NeuroImage 129:460–69
    
28. 
    Wu G, Kim M, Wang Q, Munsell BC, Shen D. 2016. Scalable high-performance image registration framework by unsupervised deep feature representations learning. IEEE Trans. Biomed. Eng. 63:1505–16
    
29. 
    Suk HI, Lee SW, Shen D. 2014. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage 101:569–82
    
30. 
    
    Annu. Rev. Biomed. Eng. 2017.19:221-248. Downloaded from www.annualreviews.org Access provided by 82.215.98.77 on 06/08/22. For personal use only.
    Shin H, Roberts K, Lu L, Demner-Fushman D, Yao J, Summers RM. 2016. Learning to read chest X-rays: recurrent neural cascade model for automated image annotation. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2497–506. Washington, DC: IEEE
    
31. 
    Suk HI, Lee SW, Shen D. 2015. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct. Funct. 220:841–59
    
32. 
    Suk HI, Shen D. 2015. Deep learning in diagnosis of brain disorders. In Recent Progress in Brain and Cognitive Engineering, ed. SW Lee, HH Bu¨ lthoff, KR Mu¨ ller, pp. 203–13. Berlin: Springer
    
33. 
    Suk HI, Wee CY, Lee SW, Shen D. 2016. State-space model with deep learning for functional dynamics estimation in resting-state fMRI. NeuroImage 129:292–307
    
34. 
    Pereira S, Pinto A, Alves V, Silva CA. 2016. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35:1240–51
    
35. 
    van Tulder G, de Bruijne M. 2016. Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines. IEEE Trans. Med. Imaging 35:1262–72
    
36. 
    Dou Q, Chen H, Yu L, Zhao L, Qin J, et al. 2016. Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans. Med. Imaging 35:1182–95
    
37. 
    Cires¸an DC, Giusti A, Gambardella LM, Schmidhuber J. 2013. Mitosis detection in breast cancer his- tological images with deep neural networks. In Proceedings of the 2013 Medical Image Computing and Computer-Assisted Intervention Conference, pp. 411–18. Berlin: Springer
    
38. 
    Chen H, Dou Q, Wang X, Qin J, Heng PA. 2016. Mitosis detection in breast cancer histology images via deep cascaded networks. In Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 1167–73. Palo Alto, CA: AAAI
    
39. 
    Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, et al. 2016. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci. Rep. 6:24454
    
40. 
    Roth HR, Lu L, Liu J, Yao J, Seff A, et al. 2016. Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans. Med. Imaging 35:1170–81
    
41. 
    Shen W, Zhou M, Yang F, Yang C, Tian J. 2015. Multi-scale convolutional neural networks for lung nodule classification. In Lecture Notes in Computer Science, vol. 9123: Information Processing in Medical Imaging, pp. 588–99. Berlin: Springer
    
42. 
    Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, et al. 2016. Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging 35:1160–69
    
43. 
    Ciompi F, de Hoop B, van Riel SJ, Chung K, Scholten ET, et al. 2015. Automatic classification of pul- monary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box. Med. Image Anal. 26:195–202
    
44. 
    Li R, Zhang W, Suk HI, Wang L, Li J, et al. 2014. Deep learning based imaging data completion for improved brain disease diagnosis. In Proceedings of the 2014 Medical Image Computing and Computer-Assisted Intervention Conference, pp. 305–12. Berlin: Springer
    
45. 
    Shin HC, Roth HR, Gao M, Lu L, Xu Z, et al. 2016. Deep convolutional neural networks for computer- aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35:1285–98
    
46. 
    Gupta A, Ayhan M, Maida A. 2013. Natural image bases to represent neuroimaging data. In Proceedings of the 30th International Conference on Machine Learning, pp. 987–94. New York: ACM
    
47. 
    Brosch T, Tam R. 2013. Manifold learning of brain MRIs by deep learning. In Proceedings of the 2013 Medical Image Computing and Computer-Assisted Intervention Conference, pp. 633–40. Berlin: Springer
    
48. 
    Nie D, Wang L, Gao Y, Shen D. 2016. Fully convolutional networks for multi-modality isointense infant brain image segmentation. In Proceedings of the 13th IEEE International Symposium on Biomedical Imaging, pp. 1342–45. Washington, DC: IEEE
    
49. 
    Brosch T, Tang LYW, Yoo Y, Li DKB, Traboulsee A, Tam R. 2016. Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmenta- tion. IEEE Trans. Med. Imaging 35:1229–39
    
50. 
    
    Annu. Rev. Biomed. Eng. 2017.19:221-248. Downloaded from www.annualreviews.org Access provided by 82.215.98.77 on 06/08/22. For personal use only.
    Chen H, Dou Q, Wang X, Qin J, Heng P. 2016. Mitosis detection in breast cancer histological images via deep cascaded networks. In Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 1160–66. Palo Alto, CA: AAAI
    
51. 
    Shin HC, Orton MR, Collins DJ, Doran SJ, Leach MO. 2013. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Trans. Pattern Anal. Mach. Intell. 35:1930–43
    
52. 
    Wu G, Kim M, Wang Q, Gao Y, Liao S, Shen D. 2013. Unsupervised deep feature learning for deformable registration of MR brain images. In Proceedings of the 2013 Medical Image Computing and Computer-Assisted Intervention Conference, pp. 649–56. Berlin: Springer
    
53. 
    Su H, Xing F, Kong X, Xie Y, Zhang S, Yang L. 2015. Robust cell detection and segmentation in histopathological images using sparse reconstruction and stacked denoising autoencoders. In Proceedings of the 2015 Medical Image Computing and Computer-Assisted Intervention Conference, pp. 383–90. Berlin: Springer
    
54. 
    Xu J, Xiang L, Liu Q, Gilmore H, Wu J, et al. 2016. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans. Med. Imaging 35:119–30
    
55. 
    Salakhutdinov R. 2015. Learning deep generative models. Annu. Rev. Stat. Appl. 2:361–85
    
56. 
    Munsell BC, Wee CY, Keller SS, Weber B, Elger C, et al. 2015. Evaluation of machine learning algo- rithms for treatment outcome prediction in patients with epilepsy based on structural connectome data. NeuroImage 118:219–30
    
57. 
    Maier O, Schrder C, Forkert ND, Martinetz T, Handels H. 2015. Classifiers for ischemic stroke lesion segmentation: a comparison study. PLOS ONE 10:1–16
    
58. 
    Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, et al. 2017. Brain tumor segmentation with deep neural networks. Med. Image Anal. 35:18–31
    
59. 
    Ronneberger O, Fischer P, Brox T. 2015. U-net: convolutional networks for biomedical image segmen- tation. In Proceedings of the 2015 Medical Image Computing and Computer-Assisted Intervention Conference, pp. 234–41. Berlin: Springer
    
60. 
    Fakhry A, Peng H, Ji S. 2016. Deep models for brain EM image segmentation: novel insights and improved performance. Bioinformatics 32:2352–58
    
61. 
    Farag A, Lu L, Roth HR, Liu J, Turkbey E, Summers RM. 2015. A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling. arXiv:1505.06236 [cs.CV]
    
62. 
    Ghesu FC, Krubasik E, Georgescu B, Singh V, Zheng Y, et al. 2016. Marginal space deep learning: efficient architecture for volumetric image parsing. IEEE Trans. Med. Imaging 35:1217–28
    
63. 
    Wang CW, Huang CT, Lee JH, Li CH, Chang SW, et al. 2016. A benchmark for comparison of dental radiography analysis algorithms. Med. Image Anal. 31:63–76
    
64. 
    Rosenblatt F. 1958. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 1958:65–386
    
65. 
    Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors.