[1] |
X. Li, H. Chen, X. Qi, Q. Dou, C. W. Fu, P. A. Heng , “H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes,” IEEE Transactions on Medical Imaging, Vol. 37, No. 12, pp. 2663-2674, 2018
doi: 10.1109/TMI.2018.2845918
pmid: 29994201
|
[2] |
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition, ” arXiv Preprint, arXiv: 1409. 1556, 2014
|
[3] |
D. Morley , “Visionary Ophthalmics: Confluence of Computer Vision and Deep Learning for Ophthalmology, ” 2018
|
[4] |
J. Schreier , “Anatomical Segmentation of CT images for Radiation Therapy planning Using Deep Learning, ” 2018
|
[5] |
A. Sevastopolsky , “Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network,” Pattern Recognition and Image Analysis,Vol. 27, No. 3, pp. 618-624, 2017
doi: 10.1134/S1054661817030269
|
[6] |
V. Dumoulin and F. Visin , “A Guide to Convolution Arithmetic for Deep Learning, ” arXiv Preprint, arXiv: 1603. 07285, 2016
|
[7] |
F. C. Ghesu, B. Georgescu, S. Grbic, A. Maier, J. Hornegger, D. Comaniciu , “Towards Intelligent Robust Detection of Anatomical Structures in Incomplete Volumetric Data,” Medical Image Analysis, Vol. 48, pp. 203-213, 2018
doi: 10.1016/j.media.2018.06.007
|
[8] |
S. Iqbal, M. U. Ghani, T. Saba, A. Rehman , “Brain Tumor Segmentation in Multi-Spectral MRI Using Convolutional Neural Networks (CNN),” Microscopy Research and Technique,Vol. 81, No. 4, pp. 419-427, 2018
doi: 10.1002/jemt.22994
pmid: 29356229
|
[9] |
M. Ghafoorian , “Machine Learning for Quantification of Small Vessel Disease Imaging Biomarkers, ” 2018
|
[10] |
J. Redmon and A. Farhadi,“YOLO9000: better, faster, stronger, ” arXiv Preprint, 2017
doi: 10.1109/CVPR.2017.690
|
[11] |
J. W. Johnson, “Adapting Mask-RCNN for Automatic Nucleus Segmentation, ” arXiv Preprint, arXiv: 1805. 00500, 2018
|
[12] |
P. Jansson , “Single-Word Speech Recognition with Convolutional Neural Networks on Raw Waveforms, ” 2018
|
[13] |
F. Yu and V. Koltun, “Multi-Scale Context aggregation by Dilated Convolutions, ” arXiv Preprint, arXiv: 1511. 07122, 2015
|
[14] |
P. L. Combettes and J. C. Pesquet, “Deep Neural Network Structures Solving Variational Inequalities, ” arXivPreprint arXiv: 1808. 07526, 2018
|
[15] |
Z. Li and N. Snavely, “Learning Intrinsic Image Decomposition from Watching the World, ” arXiv Preprint, arXiv: 1804. 00582, 2018
|
[16] |
C. Schenck and D. Fox, “Perceiving and Reasoning about Liquids Using Fully Convolutional Networks,” The International Journal of Robotics Research, Vol. 37, No.4-5, pp. 452-471, 2018
doi: 10.1177/0278364917734052
|
[17] |
I. Goodfellow , “NIPS 2016 Tutorial: Generative Adversarial Networks,” arXiv Preprint,arXiv:1701. 00160, 2016
|
[18] |
C. Li, X. Wang, W. Liu, L. J. Latecki , “DeepMitosis: Mitosis Detection via Deep Detection, Verification and Segmentation Networks,” Medical image Analysis, Vol. 45, pp. 121-133, 2018
doi: 10.1016/j.media.2017.12.002
pmid: 29455111
|
[19] |
F. Xing and L. Yang ,“RobustNucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review,” IEEE Reviews in Biomedical Engineering,Vol. 9, pp. 234-263, 2016
doi: 10.1109/RBME.2016.2515127
pmid: 26742143
|
[20] |
S. Li and G. K. F. Tso. Bottleneck , “Supervised U-Net for Pixel-Wise Liver and Tumor Segmentation, ” arXiv Preprint, arXiv: 1810. 10331, 2018
|
[21] |
Y. Wang, Y. Zhou, W. Shen, S. Park, E. K. Fishman, A. L. Yuille, “Abdominal Multi-Organ Segmentation with Organ-Attention Networks And Statistical Fusion, ” arXivPreprint, arXiv: 1804. 08414, 2018
|