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Medical image segmentation is important for computer-aided diagnosis.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Rim-one: An open retinal image database for optic nerve evaluation
F. Fumero, S. Alayon, J. L. Sanchez, J. Sigut, and M. Gonzalez-Hernandez · 2011
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The multimodal brain tumor image segmentation benchmark (brats)
Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, et al · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis
Jayanthi Sivaswamy, Subbaiah Krishnadas, Arunava Chakravarty, Gopal Joshi, and Ujjwal · 2015
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3d u-net: Learning dense volumetric segmentation from sparse annotation
Özgün Çiçek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox, and Olaf Ronneberger · 2016
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Understanding the effective receptive field in deep convolutional neural networks
Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
F. Milletari, N. Navab, and S. Ahmadi · 2016
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Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. S. Kirby, J. B. Freymann, K. Farahani, and C. Davatzikos · 2017
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Quo vadis, action recognition? a new model and the kinetics dataset
J. Carreira and A. Zisserman · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, undefinedukasz Kaiser, and Illia Polosukhin · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Path aggregation network for instance segmentation
S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia · 2018
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Attention u-net: Learning where to look for the pancreas
Ozan Oktay, Jo Schlemper, Loïc Le Folgoc, Matthew C. H. Lee, Mattias P. Heinrich, Kazunari Misawa, Kensaku Mori, et al · 2018
Cited alongside, same era.
Unet++: A nested u-net architecture for medical image segmentation
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang · 2018
Cited alongside, same era.
Dunet: A deformable network for retinal vessel segmentation
Qiangguo Jin, Zhaopeng Meng, Tuan D. Pham, Qi Chen, Leyi Wei, and Ran Su · 2019
Cited alongside, same era.
Panoptic segmentation
Alexander Kirillov, Kaiming He, Ross B. Girshick, Carsten Rother, and Piotr Dollár · 2019
Cited alongside, same era.
Set transformer: A framework for attention-based permutation-invariant neural networks
Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh · 2019
Cited alongside, same era.
Attention gated networks: Learning to leverage salient regions in medical images
Unet 3+: A full-scale connected unet for medical image segmentation
H. Huang, L. Lin, R. Tong, H. Hu, Q. Zhang, Y. Iwamoto, X. Han, Y. Chen, and J. Wu · 2020
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How much position information do convolutional neural networks encode?
Md Amirul Islam, Sen Jia, and Neil DB Bruce · 2020
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Improving brain tumor segmentation with multi-direction fusion and fine class prediction
Sun’ao Liu and Xiaonan Guo · 2020
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How Can CNNs Use Image Position for Segmentation?
Rito Murase, Masanori Suganuma, and Takayuki Okatani · 2020
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Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
José Ignacio Orlando, Huazhu Fu, João Barbosa Breda, Karel van Keer, Deepti R. Bathula, Andrés Diaz-Pinto, et al · 2020
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Jo Schlemper, Ozan Oktay, Michiel Schaap, Mattias Heinrich, Bernhard Kainz, Ben Glocker, and Daniel Rueckert · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 2019
Cited alongside, same era.
Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov · 2019
Cited alongside, same era.
3d u-net based brain tumor segmentation and survival days prediction
Feifan Wang, Runzhou Jiang, Liqin Zheng, Chun Meng, and Bharat Biswal · 2019
Cited alongside, same era.
Bag of tricks for 3d mri brain tumor segmentation
Yuan-Xing Zhao, Yan-Ming Zhang, and Cheng-Lin Liu · 2019
Cited alongside, same era.
Eff-unet: A novel architecture for semantic segmentation in unstructured environment
B. Baheti, S. Innani, S. Gajre, and S. Talbar · 2020
Cited alongside, same era.
End-to-End Object Detection with Transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
Cited alongside, same era.
Attention-based transformers for instance segmentation of cells in microstructures
Tim Prangemeier, Christoph Reich, and Heinz Koeppl · 2020
Later among the works it cites.
Efficientdet: Scalable and efficient object detection
Mingxing Tan, Ruoming Pang, and Quoc V. Le · 2020
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Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren Ng · 2020
Later among the works it cites.
Feature pyramid transformer
Dong Zhang, Hanwang Zhang, Jinhui Tang, Meng Wang, Xiansheng Hua, and Qianru Sun · 2020
Later among the works it cites.
Resnest: Split-attention networks
Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Muller, R. Manmatha, Mu Li, and Alexander Smola · 2020
Later among the works it cites.
Transunet: Transformers make strong encoders for medical image segmentation
Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan L Yuille, and Yuyin Zhou · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, et al · 2021
Closest in time.
nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Fabian Isensee, Paul F. Jaeger, Simon A. A. Kohl, Jens Petersen, and Klaus H. Maier-Hein · 2021
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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip H.S. Torr, and Li Zhang · 2021
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