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Positron Emission Tomography (PET) and Computer Tomography (CT) are routinely used together to detect tumors.
Pet/ct today and tomorrow
David W Townsend, Jonathan PJ Carney, Jeffrey T Yap, and Nathan C Hall · 2004
Earlier work this paper cites.
Image-guided cancer therapy using pet/ct
Jeffrey T Yap, Jonathan PJ Carney, Nathan C Hall, and David W Townsend · 2004
Earlier work this paper cites.
18f-fdg pet and pet/ct in the evaluation of cancer treatment response
Simona Ben-Haim and Peter Ell · 2009
Earlier work this paper cites.
3d-ssim for video quality assessment
Kai Zeng and Zhou Wang · 2012
Earlier work this paper cites.
Multi-stage thresholded region classification for whole-body pet-ct lymphoma studies
Lei Bi, Jinman Kim, Dagan Feng, and Michael Fulham · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper 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 · 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.
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.
V-net: Fully convolutional neural networks for volumetric medical image segmentation
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi · 2016
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Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge
Fabian Isensee, Philipp Kickingereder, Wolfgang Wick, Martin Bendszus, and Klaus H Maier-Hein · 2017
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Multi-task learning using multi-modal encoder-decoder networks with shared skip connections
Ryohei Kuga, Asako Kanezaki, Masaki Samejima, Yusuke Sugano, and Yasuyuki Matsushita · 2017
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Disentangling by partitioning: A representation learning framework for multimodal sensory data
Wei-Ning Hsu and James Glass · 2018
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Alex Kendall, Yarin Gal, and Roberto Cipolla · 2018
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Learning factorized multimodal representations
Yao-Hung Hubert Tsai, Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency, and Ruslan Salakhutdinov · 2018
Earlier work this paper cites.
Multi-modal learning from unpaired images: Application to multi-organ segmentation in ct and mri
Vanya V Valindria, Nick Pawlowski, Martin Rajchl, Ioannis Lavdas, Eric O Aboagye, Andrea G Rockall, Daniel Rueckert, and Ben Glocker · 2018
Earlier work this paper cites.
Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network
Zizhao Zhang, Lin Yang, and Yefeng Zheng · 2018
Earlier work this paper cites.
Deep learning-based image segmentation on multimodal medical imaging
Zhe Guo, Xiang Li, Heng Huang, Ning Guo, and Quanzheng Li · 2019
Cited alongside, same era.
Floors are flat: Leveraging semantics for real-time surface normal prediction
Steven Hickson, Karthik Raveendran, Alireza Fathi, Kevin Murphy, and Irfan Essa · 2019
Cited alongside, same era.
Cross-modality (ct-mri) prior augmented deep learning for robust lung tumor segmentation from small mr datasets
Jue Jiang, Yu-Chi Hu, Neelam Tyagi, Pengpeng Zhang, Andreas Rimner, Joseph O Deasy, and Harini Veeraraghavan · 2019
Cited alongside, same era.
Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task
Zeyu Jiang, Changxing Ding, Minfeng Liu, and Dacheng Tao · 2019
Cited alongside, same era.
Attentive single-tasking of multiple tasks
Kevis-Kokitsi Maninis, Ilija Radosavovic, and Iasonas Kokkinos · 2019
Cited alongside, same era.
A fully automated multimodal mri-based multi-task learning for glioma segmentation and idh genotyping
Jianhong Cheng, Jin Liu, Hulin Kuang, and Jianxin Wang · 2022
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A whole-body fdg-pet/ct dataset with manually annotated tumor lesions
Sergios Gatidis, Tobias Hepp, Marcel Früh, Christian La Fougère, Konstantin Nikolaou, Christina Pfannenberg, Bernhard Schölkopf, Thomas Küstner, Clemens Cyran, and Daniel Rubin · 2022
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Lars Heiliger, Zdravko Marinov, Max Hasin, André Ferreira, Jana Fragemann, Kelsey Pomykala, Jacob Murray, David Kersting, Victor Alves, Rainer Stiefelhagen, et al · 2022
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Sharing decoders: Network fission for multi-task pixel prediction
Steven Hickson, Karthik Raveendran, and Irfan Essa · 2022
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Pawel Mlynarski, Hervé Delingette, Antonio Criminisi, and Nicholas Ayache · 2019
Cited alongside, same era.
3d mri brain tumor segmentation using autoencoder regularization
Andriy Myronenko · 2019
Cited alongside, same era.
Multi-task learning for brain tumor segmentation
Leon Weninger, Qianyu Liu, and Dorit Merhof · 2019
Cited alongside, same era.
Self-derived organ attention for unpaired ct-mri deep domain adaptation based mri segmentation
Jue Jiang, Yu-Chi Hu, Neelam Tyagi, Chuang Wang, Nancy Lee, Joseph O Deasy, Berry Sean, and Harini Veeraraghavan · 2020
Cited alongside, same era.
Mmtm: Multimodal transfer module for cnn fusion
Hamid Reza Vaezi Joze, Amirreza Shaban, Michael L Iuzzolino, and Kazuhito Koishida · 2020
Cited alongside, same era.
Multi-task deep segmentation and radiomics for automatic prognosis in head and neck cancer
Vincent Andrearczyk, Pierre Fontaine, Valentin Oreiller, Joel Castelli, Mario Jreige, John O Prior, and Adrien Depeursinge · 2021
Cited alongside, same era.
Multimodal pet/ct tumour segmentation and prediction of progression-free survival using a full-scale unet with attention
Emmanuelle Bourigault, Daniel R McGowan, Abolfazl Mehranian, and Bartłomiej W Papież · 2021
Cited alongside, same era.
Paul Pu Liang, Amir Zadeh, and Louis-Philippe Morency · 2022
Later among the works it cites.
Sf-net: A multi-task model for brain tumor segmentation in multimodal mri via image fusion
Yu Liu, Fuhao Mu, Yu Shi, and Xun Chen · 2022
Later among the works it cites.
Autopet challenge 2022: Step-by-step lesion segmentation in whole-body fdg-pet/ct
Zhantao Liu, Shaonan Zhong, and Junyang Mo · 2022
Later among the works it cites.
Modselect: Automatic modality selection for synthetic-to-real domain generalization
Zdravko Marinov, Alina Roitberg, David Schneider, and Rainer Stiefelhagen · 2022
Later among the works it cites.
Deepmts: Deep multi-task learning for survival prediction in patients with advanced nasopharyngeal carcinoma using pretreatment pet/ct
Mingyuan Meng, Bingxin Gu, Lei Bi, Shaoli Song, David Dagan Feng, and Jinman Kim · 2022
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Yige Peng, Jinman Kim, Dagan Feng, and Lei Bi · 2022
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Expansion-squeeze-excitation fusion network for elderly activity recognition
Xiangbo Shu, Jiawen Yang, Rui Yan, and Yan Song · 2022
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Ludovic Sibille, Xinrui Zhan, and Lei Xiang · 2022
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Automated detection and quantification of neuroendocrine tumors on 68ga-dotatate pet/ct images using a u-net ensemble method, 2022
Amy Weisman, Ojaswita Lokre, Brayden Schott, Victor Fernandes, Robert Jeraj, Timothy Perk, Steve Cho, and Scott Perlman · 2022
Later among the works it cites.
Exploring vanilla u-net for lesion segmentation from whole-body fdg-pet/ct scans
Jin Ye, Haoyu Wang, Ziyan Huang, Zhongying Deng, Yanzhou Su, Can Tu, Qian Wu, Yuncheng Yang, Meng Wei, Jingqi Niu, et al · 2022
Later among the works it cites.
Whole-body lesion segmentation in 18f-fdg pet/ct
Jia Zhang, Yukun Huang, Zheng Zhang, and Yuhang Shi · 2022
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Shaonan Zhong, Junyang Mo, and Zhantao Liu · 2022
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Verena Jasmin Hallitschke, Tobias Schlumberger, Philipp Kataliakos, Zdravko Marinov, Moon Kim, Lars Heiliger, Constantin Seibold, Jens Kleesiek, and Rainer Stiefelhagen · 2023
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