Fetching the paper…
Reading the bibliography…
Incomplete multi-modal image segmentation is a fundamental task in medical imaging to refine deployment efficiency when only partial modalities are available.
PET/MRI assessment of lung nodules in primary abdominal malignancies: sensitivity and outcome analysis
Pierpaolo Biondetti, Mark G Vangel, Rita M Lahoud, Felipe S Furtado, Bruce R Rosen, David Groshar, Lina G Canamaque, Lale Umutlu, Eric W Zhang, Umar Mahmood, et al · 1986
Earlier work this paper cites.
A survey of MRI-based medical image analysis for brain tumor studies
Stefan Bauer, Roland Wiest, Lutz-P Nolte, and Mauricio Reyes. 2013 · 2013
Earlier work this paper cites.
Body MRI artifacts in clinical practice: a physicist’s and radiologist’s perspective
Martin J Graves and Donald G Mitchell. 2013 · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
Earlier work this paper cites.
Artifacts in magnetic resonance imaging
Katarzyna Krupa and Monika Bekiesińska-Figatowska. 2015 · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention . 234–241
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
Earlier work this paper cites.
3D U-Net: learning dense volumetric segmentation from sparse annotation. In Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention . 424–432
Özgün Çiçek, Ahmed Abdulkadir, Soeren S Lienkamp, Thomas Brox, and Olaf Ronneberger. 2016 · 2016
Earlier work this paper cites.
Hemis: Hetero-modal image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention . 469–477
Mohammad Havaei, Nicolas Guizard, Nicolas Chapados, and Yoshua Bengio. 2016 · 2016
Earlier work this paper cites.
Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features
Spyridon Bakas, Hamed Akbari, Aristeidis Sotiras, Michel Bilello, Martin Rozycki, Justin S Kirby, John B Freymann, Keyvan Farahani, and Christos Davatzikos. 2017 · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of International Conference on Machine Learning . 1126–1135
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.
Brain tumor segmentation with deep neural networks
Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin, and Hugo Larochelle. 2017 · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
Earlier work this paper cites.
Multivariate mixture model for myocardial segmentation combining multi-source images
Xiahai Zhuang. 2018 · 2018
Earlier work this paper cites.
Dual-force convolutional neural networks for accurate brain tumor segmentation
Shengcong Chen, Changxing Ding, and Minfeng Liu. 2019a · 2019
Cited alongside, same era.
Hetero-modal variational encoder-decoder for joint modality completion and segmentation. In International Conference on Medical Image Computing and Computer Assisted Intervention . 74–82
Reuben Dorent, Samuel Joutard, Marc Modat, Sébastien Ourselin, and Tom Vercauteren. 2019 · 2019
Cited alongside, same era.
CoCa-GAN: common-feature-learning-based context-aware generative adversarial network for glioma grading. In International Conference on Medical Image Computing and Computer Assisted Intervention . 155–163
Pu Huang, Dengwang Li, Zhicheng Jiao, Dongming Wei, Guoshi Li, Qian Wang, Han Zhang, and Dinggang Shen. 2019 · 2019
Cited alongside, same era.
Missing MRI pulse sequence synthesis using multi-modal generative adversarial network
Anmol Sharma and Ghassan Hamarneh. 2019 · 2019
Cited alongside, same era.
ACN: adversarial co-training network for brain tumor segmentation with missing modalities. In International Conference on Medical Image Computing and Computer Assisted Intervention . Springer, 410–420
Yixin Wang, Yang Zhang, Yang Liu, Zihao Lin, Jiang Tian, Cheng Zhong, Zhongchao Shi, Jianping Fan, and Zhiqiang He. 2021 · 2021
Later among the works it cites.
MouseGAN: GAN-based multiple MRI modalities synthesis and segmentation for mouse brain structures. In International Conference on Medical Image Computing and Computer Assisted Intervention . 442–450
Ziqi Yu, Yuting Zhai, Xiaoyang Han, Tingying Peng, and Xiao-Yong Zhang. 2021 · 2021
Later among the works it cites.
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities. In International Conference on Medical Imaging with Deep Learning . 48–62
Reza Azad, Nika Khosravi, and Dorit Merhof. 2022 · 2022
Later among the works it cites.
Balanced multimodal learning via on-the-fly gradient modulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 8238–8247
Xiaokang Peng, Yake Wei, Andong Deng, Dong Wang, and Di Hu. 2022 · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
What makes training multi-modal classification networks hard?. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 12695–12705
Weiyao Wang, Du Tran, and Matt Feiszli. 2020 · 2020
Cited alongside, same era.
Audiovisual slowfast networks for video recognition
Fanyi Xiao, Yong Jae Lee, Kristen Grauman, Jitendra Malik, and Christoph Feichtenhofer. 2020 · 2020
Cited alongside, same era.
Exploring task structure for brain tumor segmentation from multi-modality MR images
Dingwen Zhang, Guohai Huang, Qiang Zhang, Jungong Han, Junwei Han, Yizhou Wang, and Yizhou Yu. 2020 · 2020
Cited alongside, same era.
Learning with privileged multimodal knowledge for unimodal segmentation
Cheng Chen, Qi Dou, Yueming Jin, Quande Liu, and Pheng Ann Heng. 2021 · 2021
Cited alongside, same era.
RFNet: Region-aware fusion network for incomplete multi-modal brain tumor segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 3955–3964
Yuhang Ding, Xin Yu, and Yi Yang. 2021 · 2021
Cited alongside, same era.
Improving multi-modal learning with uni-modal teachers
Chenzhuang Du, Tingle Li, Yichen Liu, Zixin Wen, Tianyu Hua, Yue Wang, and Hang Zhao. 2021 · 2021
Cited alongside, same era.
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Fabian Isensee, Paul F Jaeger, Simon AA Kohl, Jens Petersen, and Klaus H Maier-Hein. 2021 · 2021
Cited alongside, same era.
Smil: Multimodal learning with severely missing modality. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 2302–2310
Mengmeng Ma, Jian Ren, Long Zhao, Sergey Tulyakov, Cathy Wu, and Xi Peng. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
NestedFormer: Nested modality-aware transformer for brain tumor segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention . 140–150
Zhaohu Xing, Lequan Yu, Liang Wan, Tong Han, and Lei Zhu. 2022 · 2022
Later among the works it cites.
mmformer: Multimodal medical transformer for incomplete multimodal learning of brain tumor segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention . 107–117
Yao Zhang, Nanjun He, Jiawei Yang, Yuexiang Li, Dong Wei, Yawen Huang, Yang Zhang, Zhiqiang He, and Yefeng Zheng. 2022 · 2022
Later among the works it cites.
Modality-adaptive feature interaction for brain tumor segmentation with missing modalities. In International Conference on Medical Image Computing and Computer-Assisted Intervention . 183–192
Zechen Zhao, Heran Yang, and Jian Sun. 2022 · 2022
Later among the works it cites.
Pmr: Prototypical modal rebalance for multimodal learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 20029–20038
Yunfeng Fan, Wenchao Xu, Haozhao Wang, Junxiao Wang, and Song Guo. 2023 · 2023
Later among the works it cites.
Enhancing modality-agnostic representations via meta-learning for brain tumor segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 21415–21425
Aishik Konwer, Xiaoling Hu, Joseph Bae, Xuan Xu, Chao Chen, and Prateek Prasanna. 2023 · 2023
Later among the works it cites.
M3AE: Multimodal representation learning for brain tumor segmentation with missing modalities. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 37. 1657–1665
Hong Liu, Dong Wei, Donghuan Lu, Jinghan Sun, Liansheng Wang, and Yefeng Zheng. 2023 · 2023
Later among the works it cites.
MyoPS-Net: Myocardial pathology segmentation with flexible combination of multi-sequence CMR images
Junyi Qiu, Lei Li, Sihan Wang, Ke Zhang, Yinyin Chen, Shan Yang, and Xiahai Zhuang. 2023b · 2023
Later among the works it cites.
The multimodal brain tumor image segmentation benchmark (BRATS)
Bjoern H Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, et al · 2024
Closest in time.
M2FTrans: Modality-masked fusion transformer for incomplete multi-modality brain tumor segmentation
Junjie Shi, Li Yu, Qimin Cheng, Xin Yang, Kwang-Ting Cheng, and Zengqiang Yan. 2024 · 2024
Closest in time.
RedCore: Relative advantage aware cross-modal representation learning for missing modalities with imbalanced missing rates. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 15173–15182
Jun Sun, Xinxin Zhang, Shoukang Han, Yu-Ping Ruan, and Taihao Li. 2024 · 2024
Closest in time.