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Image of the Page - 145 -
Text of the Page - 145 -
[17] C. Payer, D. Sˇtern, H. Bischof, and M. Urschler, “Regressing
Heatmaps for Multiple Landmark Localization Using CNNs,” in In-
ternational Conference on Medical Image Computing and Computer-
Assisted Intervention. Springer, 2016, pp. 230–238.
[18] A. Radford, L. Metz, and S. Chintala, “Unsupervised Representation
Learning with Deep Convolutional Generative Adversarial Networks,”
in International Conference on Learning Representations, 2016.
[19] A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “CNN
Features Off-the-Shelf: An Astounding Baseline for Recognition,”
in Proceedings of the 2014 IEEE Conference on Computer Vision
and Pattern Recognition Workshops, ser. CVPRW ’14. Washington,
DC, USA: IEEE Computer Society, 2014, pp. 512–519. [Online].
Available: http://dx.doi.org/10.1109/CVPRW.2014.131
[20] G. Riegler, M. Urschler, M. Ru¨ther, H. Bischof, and D. Sˇtern,
“Anatomical Landmark Detection in Medical Applications Driven by
Synthetic Data,” in 2015 IEEE International Conference on Computer
Vision Workshop (ICCVW), Dec 2015, pp. 85–89.
[21] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional
Networks for Biomedical Image Segmentation,” in Medical
Image Computing and Computer-Assisted Intervention (MICCAI),
ser. LNCS, vol. 9351. Springer, 2015, pp. 234–241,
(available on arXiv:1505.04597 [cs.CV]). [Online]. Available:
http://lmb.informatik.uni-freiburg.de//Publications/2015/RFB15a
[22] A. Rozantsev, V. Lepetit, and P. Fua, “On Rendering Synthetic
Images for Training an Object Detector,” Computer Vision and Image
Understanding, vol. 137, pp. 24 – 37, 2015. [Online]. Available:
http://www.sciencedirect.com/science/article/pii/S1077314214002446
[23] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma,
Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and
L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,”
International Journal of Computer Vision (IJCV), vol. 115, no. 3, pp.
211–252, 2015. [24] T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford,
X. Chen, and X. Chen, “Improved Techniques for Training
GANs,” in Advances in Neural Information Processing Systems
29, D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and
R. Garnett, Eds. Curran Associates, Inc., 2016, pp. 2234–
2242. [Online]. Available: http://papers.nips.cc/paper/6125-improved-
techniques-for-training-gans.pdf
[25] J. Shiraishi, S. Katsuragawa, J. Ikezoe, T. Matsumoto, T. Kobayashi,
K.-i. Komatsu, M. Matsui, H. Fujita, Y. Kodera, and K. Doi,
“Development of a Digital Image Database for Chest Radiographs
With and Without a Lung Nodule,” American Journal of
Roentgenology, vol. 174, no. 1, pp. 71–74, Jan. 2000. [Online].
Available: http://dx.doi.org/10.2214/ajr.174.1.1740071
[26] B. van Ginneken, M. Stegmann, and M. Loog, “Segmentation of
anatomical structures in chest radiographs using supervised methods:
a comparative study on a public database,” Medical Image Analysis,
vol. 10, no. 1, pp. 19–40, 2006.
[27] P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol,
“Extracting and Composing Robust Features with Denoising
Autoencoders,” in Proceedings of the 25th International
Conference on Machine Learning, ser. ICML ’08. New York,
NY, USA: ACM, 2008, pp. 1096–1103. [Online]. Available:
http://doi.acm.org/10.1145/1390156.1390294
[28] J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are
features in deep neural networks?” in Advances in Neural Information
Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes, N. D.
Lawrence, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2014,
pp. 3320–3328. [Online]. Available: http://papers.nips.cc/paper/5347-
how-transferable-are-features-in-deep-neural-networks.pdf
145
Proceedings of the OAGM&ARW Joint Workshop
Vision, Automation and Robotics
- Title
- Proceedings of the OAGM&ARW Joint Workshop
- Subtitle
- Vision, Automation and Robotics
- Authors
- Peter M. Roth
- Markus Vincze
- Wilfried Kubinger
- Andreas MĂĽller
- Bernhard Blaschitz
- Svorad Stolc
- Publisher
- Verlag der Technischen Universität Graz
- Location
- Wien
- Date
- 2017
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-85125-524-9
- Size
- 21.0 x 29.7 cm
- Pages
- 188
- Keywords
- Tagungsband
- Categories
- International
- Tagungsbände