Fetching the paper…
Reading the bibliography…
In accelerated MRI reconstruction, the anatomy of a patient is recovered from a set of under-sampled and noisy measurements.
Multiscale structural similarity for image quality assessment
Zhou Wang, Eero P Simoncelli, and Alan C Bovik · 2003
Earlier work this paper cites.
Stable signal recovery from incomplete and inaccurate measurements
Emmanuel J. Candes, Justin K. Romberg, and Terence Tao · 2006
Earlier work this paper cites.
Compressed sensing
David L. Donoho · 2006
Earlier work this paper cites.
Compressed sensing MRI
Michael Lustig, David L. Donoho, Juan M. Santos, and John M. Pauly · 2008
Earlier work this paper cites.
Creation of fully sampled MR data repository for compressed sensing of the knee
Anne Marie Sawyer, Michael Lustig, Marcus Alley, Phdmartin Uecker, Patrick Virtue, Peng Lai, and Shreyas Vasanawala · 2013
Earlier work this paper cites.
ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA
Martin Uecker, Peng Lai, Mark J Murphy, Patrick Virtue, Michael Elad, John M Pauly, Shreyas S Vasanawala, and Michael Lustig · 2014
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.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
3D U-Net: Learning dense volumetric segmentation from sparse annotation
Özgün Çiçek, Ahmed Abdulkadir, Soeren S Lienkamp, Thomas Brox, and Olaf Ronneberger · 2016
Earlier work this paper cites.
Deep ADMM-Net for compressive sensing MRI
Jian Sun, Huibin Li, Zongben Xu, et al · 2016
Earlier work this paper cites.
Learning a variational network for reconstruction of accelerated MRI data
Kerstin Hammernik, Teresa Klatzer, Erich Kobler, Michael P Recht, Daniel K Sodickson, Thomas Pock, and Florian Knoll · 2018
Earlier work this paper cites.
Framing U-Net via deep convolutional framelets: Application to sparse-view CT
Yoseob Han and Jong Chul Ye · 2018
Earlier work this paper cites.
Deep learning for undersampled MRI reconstruction
Chang Min Hyun, Hwa Pyung Kim, Sung Min Lee, Sungchul Lee, and Jin Keun Seo · 2018
Earlier work this paper cites.
ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing
Jian Zhang and Bernard Ghanem · 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.
Kerstin Hammernik, Jo Schlemper, Chen Qin, Jinming Duan, Ronald M Summers, and Daniel Rueckert · 2019
Cited alongside, same era.
I-RIM applied to the fastMRI Challenge
Patrick Putzky, Dimitrios Karkalousos, Jonas Teuwen, Nikita Miriakov, Bart Bakker, Matthan Caan, and Max Welling · 2019
Cited alongside, same era.
Missformer: An effective medical image segmentation Transformer
Xiaohong Huang, Zhifang Deng, Dandan Li, and Xueguang Yuan · 2021
Later among the works it cites.
SwinIR: Image restoration using Swin Transformer
Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte · 2021
Later among the works it cites.
Swin Transformer: Hierarchical Vision Transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Later among the works it cites.
Eformer: Edge enhancement based Transformer for medical image denoising
Achleshwar Luthra, Harsh Sulakhe, Tanish Mittal, Abhishek Iyer, and Santosh Yadav · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J. Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzal, Adriana Romero, Michael Rabbat, Pascal Vincent, Nafissa Yakubova, James Pinkerton, Duo Wang, Erich Owens, C. Lawrence Zitnick, Michael P. Recht, Daniel K. Sodickson, and Yvonne W. Lui · 2019
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.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2020
Cited alongside, same era.
Deep learning techniques for inverse problems in imaging
Gregory Ongie, Ajil Jalal, Christopher A. Metzler Richard G. Baraniuk, Alexandros G. Dimakis, and Rebecca Willett · 2020
Cited alongside, same era.
XPDNet for MRI reconstruction: an application to the 2020 fastMRI Challenge
Zaccharie Ramzi, Philippe Ciuciu, and Jean-Luc Starck · 2020
Cited alongside, same era.
End-to-end variational networks for accelerated MRI reconstruction
Anuroop Sriram, Jure Zbontar, Tullie Murrell, Aaron Defazio, C. Lawrence Zitnick, Nafissa Yakubova, Florian Knoll, and Patricia Johnson · 2020
Cited alongside, same era.
Pre-trained image processing Transformer
Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao · 2021
Cited alongside, same era.
Task Transformer network for joint MRI reconstruction and super-resolution
Chun-Mei Feng, Yunlu Yan, Huazhu Fu, Li Chen, and Yong Xu · 2021
Cited alongside, same era.
Matthew J Muckley, Bruno Riemenschneider, Alireza Radmanesh, Sunwoo Kim, Geunu Jeong, Jingyu Ko, Yohan Jun, Hyungseob Shin, Dosik Hwang, Mahmoud Mostapha, et al · 2021
Later among the works it cites.
Restormer: Efficient Transformer for high-resolution image restoration
Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang · 2021
Later among the works it cites.
Spatial adaptive and Transformer fusion network (STFNet) for low-count PET blind denoising with MRI
Lipei Zhang, Zizheng Xiao, Chao Zhou, Jianmin Yuan, Qiang He, Yongfeng Yang, Xin Liu, Dong Liang, Hairong Zheng, Wei Fan, et al · 2021
Later among the works it cites.
nnFormer: Interleaved Transformer for volumetric segmentation
Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, and Yizhou Yu · 2021
Later among the works it cites.
Jiahao Huang, Yingying Fang, Yinzhe Wu, Huanjun Wu, Zhifan Gao, Yang Li, Javier Del Ser, Jun Xia, and Guang Yang · 2022
Closest in time.
Scaling laws for deep learning based image reconstruction
Tobit Klug and Reinhard Heckel · 2022
Closest in time.
Unsupervised MRI reconstruction via zero-shot learned adversarial Transformers
Yilmaz Korkmaz, Salman UH Dar, Mahmut Yurt, Muzaffer Özbey, and Tolga Cukur · 2022
Closest in time.
Vision Transformers enable fast and robust accelerated MRI
Kang Lin and Reinhard Heckel · 2022
Closest in time.
D-Former: A U-shaped dilated Transformer for 3D medical image segmentation
Yixuan Wu, Kuanlun Liao, Jintai Chen, Danny Z Chen, Jinhong Wang, Honghao Gao, and Jian Wu · 2022
Closest in time.