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Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks.
A nonparametric method for automatic correction of intensity nonuniformity in mri data
John G Sled, Alex P Zijdenbos, and Alan C Evans · 1998
Earlier work this paper cites.
Curves and surfaces in geometric modeling: theory and algorithms
Jean Gallier and Jean H Gallier · 2000
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N4ITK: improved N3 bias correction
Nicholas J Tustison, Brian B Avants, Philip A Cook, Yuanjie Zheng, Alexander Egan, Paul A Yushkevich, and James C Gee · 2010
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Medical image segmentation on gpus–a comprehensive review
Erik Smistad, Thomas L Falch, Mohammadmehdi Bozorgi, Anne C Elster, and Frank Lindseth · 2015
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Deep learning in medical image analysis
Dinggang Shen, Guorong Wu, and Heung-Il Suk · 2017
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A survey on deep learning in medical image analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2017
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Generalizability vs. robustness: adversarial examples for medical imaging
Magdalini Paschali, Sailesh Conjeti, Fernando Navarro, and Nassir Navab · 2018
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Technical report on the cleverhans v2.1.0 adversarial examples library
Nicolas Papernot, Fartash Faghri, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Alexey Kurakin, Cihang Xie, Yash Sharma, Tom Brown, Aurko Roy, Alexander Matyasko, Vahid Behzadan, Karen Hambardzumyan, Zhishuai Zhang, Yi-Lin Juang, Zhi Li, Ryan Sheatsley, Abhibhav Garg, Jonathan Uesato, Willi Gierke, Yinpeng Dong, David Berthelot, Paul Hendricks, Jonas Rauber, and Rujun Long · 2018
Earlier work this paper cites.
Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, and Silvio Savarese · 2018
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Virtual adversarial training: A regularization method for supervised and Semi-Supervised learning
Takeru Miyato, Shin-Ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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Generalizability vs. robustness: Adversarial examples for medical imaging
Magdalini Paschali, Sailesh Conjeti, Fernando Navarro, and Nassir Navab · 2018
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Geometric robustness of deep networks: Analysis and improvement
Can Kanbak, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2018
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Deep learning techniques for automatic MRI cardiac Multi-Structures segmentation and diagnosis: Is the problem solved?
Olivier Bernard, Alain Lalande, and Pierre-Marc others · 2018
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Adversarial pulmonary pathology translation for pairwise chest x-ray data augmentation
Yunyan Xing, Zongyuan Ge, Rui Zeng, Dwarikanath Mahapatra, Jarrel Seah, Meng Law, and Tom Drummond · 2019
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Automatic brain tissue segmentation in fetal mri using convolutional neural networks
Nadieh Khalili, Nikolas Lessmann, Elise Turk, N Claessens, Roel de Heus, Tessel Kolk, Max A Viergever, Manon JNL Benders, and Ivana Išgum · 2019
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Intelligent image synthesis to attack a segmentation CNN using adversarial learning
Liang Chen, Paul Bentley, Kensaku Mori, Kazunari Misawa, Michitaka Fujiwara, and Daniel Rueckert · 2019
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Adversarial training and robustness for multiple perturbations
Florian Tramèr and Dan Boneh · 2019
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Exploring the landscape of spatial robustness
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2019
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AirLab: Autograd image registration laboratory
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
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Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey Chiang, Zhihao Wu, and Xiaowei Ding · 2019
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Data augmentation using learned transformations for one-shot medical image segmentation
Amy Zhao, Guha Balakrishnan, Fredo Durand, John V Guttag, and Adrian V Dalca · 2019
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Active appearance model induced generative adversarial network for controlled data augmentation
Jianfei Liu, Christine Shen, Tao Liu, Nancy Aguilera, and Johnny Tam · 2019
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Semi-Supervised and Task-Driven data augmentation
Krishna Chaitanya, Neerav Karani, Christian F Baumgartner, Anton Becker, Olivio Donati, and Ender Konukoglu · 2019
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Adversarial attacks beyond the image space
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Adef: an iterative algorithm to construct adversarial deformations
Rima Alaifari, Giovanni S. Alberti, and Tandri Gauksson · 2019
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Unsupervised multi-modal style transfer for cardiac mr segmentation
Chen Chen, Cheng Ouyang, Giacomo Tarroni, Jo Schlemper, Huaqi Qiu, Wenjia Bai, and Daniel Rueckert · 2019
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Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
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A geometric understanding of deep learning
Na Lei, Dongsheng An, Yang Guo, Kehua Su, Shixia Liu, Zhongxuan Luo, Shing-Tung Yau, and Xianfeng Gu · 2020
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