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Liver lesion segmentation is an important step for liver cancer diagnosis, treatment planning and treatment evaluation.
“Efficient inference in fully connected CRFs with Gaussian edge potentials,”
Philipp Krähenbühl and Vladlen Koltun, · 2011
Earlier work this paper cites.
“Caffe: Convolutional architecture for fast feature embedding,”
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell, · 2014
Earlier work this paper cites.
“U-Net: Convolutional networks for biomedical image segmentation,”
Olaf Ronneberger, Philipp Fischer, and Thomas Brox, · 2015
Earlier work this paper cites.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2015
Earlier work this paper cites.
“Fully convolutional networks for semantic segmentation,”
Jonathan Long, Evan Shelhamer, and Trevor Darrell, · 2015
Cited alongside, same era.
“Learning deconvolution network for semantic segmentation,”
Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han, · 2015
Cited alongside, same era.
“SegNet: A deep convolutional encoder-decoder architecture for image segmentation,”
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla, · 2015
Cited alongside, same era.
“Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2015
Cited alongside, same era.
“Batch normalization: Accelerating deep network training by reducing internal covariate shift,”
Sergey Ioffe and Christian Szegedy, · 2015
Later among the works it cites.
“Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields,”
Patrick Ferdinand Christ, Mohamed Ezzeldin A. Elshaer, Florian Ettlinger, Sunil Tatavarty, Marc Bickel, Patrick Bilic, Markus Rempfler, Marco Armbruster, Felix Hofmann, Melvin D’Anastasi, Wieland H. Sommer, Seyed-Ahmad Ahmadi, and Bjoern H. Menze, · 2016
Later among the works it cites.
“V-Net: Fully convolutional neural networks for volumetric medical image segmentation,”
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi, · 2016
Later among the works it cites.
“A survey on deep learning in medical image analysis,”
Geert J. S. Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A. W. M. van der Laak, Bram van Ginneken, and Clara I. Sánchez, · 2017
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