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This study leverages synthetic data as a validation set to reduce overfitting and ease the selection of the best model in AI development.
The liver tumor segmentation benchmark (lits)
Patrick Bilic, Patrick Ferdinand Christ, Eugene Vorontsov, Grzegorz Chlebus, Hao Chen, Qi Dou, Chi-Wing Fu, Xiao Han, Pheng-Ann Heng, Jürgen Hesser, et al. 2019 · 1901
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Three scenarios for continual learning
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A review of image-warping methods
Chris A Glasbey and Kantilal Vardichand Mardia. 1998 · 1998
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Triple-phase mdct of hepatocellular carcinoma
KHY Lee, ME O’Malley, MA Haider, and A Hanbidge. 2004 · 2004
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Pattern recognition and neural networks
Brian D Ripley. 2007 · 2007
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston. 2008 · 2008
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Digital image processing
Rafael C Gonzalez. 2009 · 2009
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Artificial intelligence a modern approach
Stuart J Russell. 2010 · 2010
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An introduction to statistical learning: with applications in R
James Gareth, Witten Daniela, Hastie Trevor, and Tibshirani Robert. 2013 · 2013
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Applied predictive modeling , volume 26
Max Kuhn, Kjell Johnson, et al. 2013 · 2013
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2015 miccai multi-atlas labeling beyond the cranial vault workshop and challenge
B Landman, Z Xu, J Igelsias, M Styner, T Langerak, and A Klein. 2015 · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016 · 2016
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Data from pancreas-ct
Holger Roth, Amal Farag, Evrim B. Turkbey, Le Lu, Jiamin Liu, and Ronald M. Summers. 2016 · 2016
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. 2017 · 2017
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Liver imaging reporting and data system (LI-RADS) version 2018: Imaging of hepatocellular carcinoma in at-risk patients
Victoria Chernyak, Kathryn J Fowler, Aya Kamaya, Ania Z Kielar, Khaled M Elsayes, Mustafa R Bashir, Yuko Kono, Richard K Do, Donald G Mitchell, Amit G Singal, An Tang, and Claude B Sirlin. 2018 · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola. 2018 · 2018
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Pate-gan: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela Van Der Schaar. 2018 · 2018
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Abnormal colon polyp image synthesis using conditional adversarial networks for improved detection performance
Younghak Shin, Hemin Ali Qadir, and Ilangko Balasingham. 2018 · 2018
Cited alongside, same era.
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 · 2018
Cited alongside, same era.
Learning semantic segmentation from synthetic data: A geometrically guided input-output adaptation approach
Yuhua Chen, Wen Li, Xiaoran Chen, and Luc Van Gool. 2019 · 2019
Cited alongside, same era.
Time-series generative adversarial networks
Jinsung Yoon, Daniel Jarrett, and Mihaela Van der Schaar. 2019 · 2019
Cited alongside, same era.
Learning from synthetic animals
Jiteng Mu, Weichao Qiu, Gregory D Hager, and Alan L Yuille. 2020 · 2020
Cited alongside, same era.
The challenges of continuous self-supervised learning
Senthil Purushwalkam, Pedro Morgado, and Abhinav Gupta. 2022 · 2022
Later among the works it cites.
Self-supervised pre-training of swin transformers for 3d medical image analysis
Yucheng Tang, Dong Yang, Wenqi Li, Holger R Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh. 2022 · 2022
Later among the works it cites.
Three types of incremental learning
Gido M Van de Ven, Tinne Tuytelaars, and Andreas S Tolias. 2022 · 2022
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Anomaly segmentation in retinal images with poisson-blending data augmentation
Hualin Wang, Yuhong Zhou, Jiong Zhang, Jianqin Lei, Dongke Sun, Feng Xu, and Xiayu Xu. 2022 · 2022
Later among the works it cites.
Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise
Julian Wyatt, Adam Leach, Sebastian M Schmon, and Chris G Willcocks. 2022 · 2022
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Interpreting medical images
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Quant gans: deep generation of financial time series
Magnus Wiese, Robert Knobloch, Ralf Korn, and Peter Kretschmer. 2020 · 2020
Cited alongside, same era.
Synthetic data in machine learning for medicine and healthcare
Richard J Chen, Ming Y Lu, Tiffany Y Chen, Drew FK Williamson, and Faisal Mahmood. 2021 · 2021
Cited alongside, same era.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021 · 2021
Cited alongside, same era.
Label-free segmentation of covid-19 lesions in lung ct
Qingsong Yao, Li Xiao, Peihang Liu, and S Kevin Zhou. 2021 · 2021
Cited alongside, same era.
Towards Annotation-Efficient Deep Learning for Computer-Aided Diagnosis
Zongwei Zhou. 2021 · 2021
Cited alongside, same era.
Early detection of cancer
David Crosby, Sangeeta Bhatia, Kevin M Brindle, Lisa M Coussens, Caroline Dive, Mark Emberton, Sadik Esener, Rebecca C Fitzgerald, Sanjiv S Gambhir, Peter Kuhn, et al. 2022 · 2022
Cited alongside, same era.
Metgan: Generative tumour inpainting and modality synthesis in light sheet microscopy
Izabela Horvath, Johannes Paetzold, Oliver Schoppe, Rami Al-Maskari, Ivan Ezhov, Suprosanna Shit, Hongwei Li, Ali Ertürk, and Bjoern Menze. 2022 · 2022
Cited alongside, same era.
Zongwei Zhou, Michael B Gotway, and Jianming Liang. 2022 · 2022
Later among the works it cites.
Assembling and exploiting large-scale existing labels of common thorax diseases for improved covid-19 classification using chest radiographs
Zengle Zhu, Mintong Kang, Alan Yuille, and Zongwei Zhou. 2022 · 2022
Later among the works it cites.
Synthetic data from diffusion models improves imagenet classification
Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad Norouzi, and David J Fleet. 2023 · 2023
Closest in time.
Bedlam: A synthetic dataset of bodies exhibiting detailed lifelike animated motion
Michael J Black, Priyanka Patel, Joachim Tesch, and Jinlong Yang. 2023 · 2023
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A data augmentation perspective on diffusion models and retrieval
Max F Burg, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, and Chris Russell. 2023 · 2023
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Synthetic data accelerates the development of generalizable learning-based algorithms for x-ray image analysis
Cong Gao, Benjamin D Killeen, Yicheng Hu, Robert B Grupp, Russell H Taylor, Mehran Armand, and Mathias Unberath. 2023 · 2023
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Label-free liver tumor segmentation
Qixin Hu, Yixiong Chen, Junfei Xiao, Shuwen Sun, Jieneng Chen, Alan L Yuille, and Zongwei Zhou. 2023 · 2023
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Label-assemble: Leveraging multiple datasets with partial labels
Mintong Kang, Bowen Li, Zengle Zhu, Yongyi Lu, Elliot K Fishman, Alan Yuille, and Zongwei Zhou. 2023 · 2023
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Early detection and localization of pancreatic cancer by label-free tumor synthesis
Bowen Li, Yu-Cheng Chou, Shuwen Sun, Hualin Qiao, Alan Yuille, and Zongwei Zhou. 2023 · 2023
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Abdomenatlas-8k: Annotating 8,000 abdominal ct volumes for multi-organ segmentation in three weeks
Chongyu Qu, Tiezheng Zhang, Hualin Qiao, Jie Liu, Yucheng Tang, Alan Yuille, and Zongwei Zhou. 2023 · 2023
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Less is more: Unsupervised mask-guided annotated ct image synthesis with minimum manual segmentations
Xiaodan Xing, Giorgos Papanastasiou, Simon Walsh, and Guang Yang. 2023 · 2023
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How good are synthetic medical images? an empirical study with lung ultrasound
Menghan Yu, Sourabh Kulhare, Courosh Mehanian, Charles B Delahunt, Daniel E Shea, Zohreh Laverriere, Ishan Shah, and Matthew P Horning. 2023 · 2023
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Toward understanding generative data augmentation
Chenyu Zheng, Guoqiang Wu, and Chongxuan Li. 2023 · 2023
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