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Extracting, harvesting and building large-scale annotated radiological image datasets is a greatly important yet challenging problem.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Discriminative clustering by regularized information maximization
R. Gomes, A. Krause, and P. Perona · 2010
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
The pascal visual object classes challenge: A retrospective
M. Everingham, A. Eslami, L. Van Gool, C. Williams, J. Winn, and A. Zisserman · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
N. Tajbakhsh, M. B. Gotway, and J. Liang · 2015
Cited alongside, same era.
Fully convolutional network for liver segmentation and lesions detection
A. Ben-Cohen, I. Diamant, E. Klang, M. Amitai, and H. Greenspan · 2016
Later among the works it cites.
Colitis detection on computed tomography using regional convolutional neural networks
J. Liu, D. Wang, Z. Wei, L. Lu, L. Kim, E. Turkbey, and R. M. Summers · 2016
Later among the works it cites.
Dermatologist-level classification of skin cancer with deep neural networks
A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun · 2017
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