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It is essential but challenging to share medical image datasets due to privacy issues, which prohibit building foundation models and knowledge transfer.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
Earlier work this paper cites.
Lenet-5, convolutional neural networks
Yann LeCun et al · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
The history began from alexnet: A comprehensive survey on deep learning approaches
Md Zahangir Alom, Tarek M Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Brian C Van Esesn, Abdul A S Awwal, and Vijayan K Asari · 2018
Earlier work this paper cites.
Loss surfaces, mode connectivity, and fast ensembling of dnns
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P Vetrov, and Andrew G Wilson · 2018
Earlier work this paper cites.
Identifying medical diagnoses and treatable diseases by image-based deep learning
Daniel S Kermany, Michael Goldbaum, Wenjia Cai, Carolina CS Valentim, Huiying Liang, Sally L Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan, et al · 2018
Earlier work this paper cites.
Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
Earlier work this paper cites.
The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Philipp Tschandl, Cliff Rosendahl, and Harald Kittler · 2018
Earlier work this paper cites.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
Earlier work this paper cites.
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
Jakob Nikolas Kather, Johannes Krisam, Pornpimol Charoentong, Tom Luedde, Esther Herpel, Cleo-Aron Weis, Timo Gaiser, Alexander Marx, Nektarios A Valous, Dyke Ferber, et al · 2019
Earlier work this paper cites.
Dataset of breast ultrasound images
Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy · 2020
Earlier work this paper cites.
Soft-label anonymous gastric x-ray image distillation
Guang Li, Ren Togo, Takahiro Ogawa, and Miki Haseyama · 2020
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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Exploring the effect of image enhancement techniques on covid-19 detection using chest x-ray images
Tawsifur Rahman, Amith Khandakar, Yazan Qiblawey, Anas Tahir, Serkan Kiranyaz, Saad Bin Abul Kashem, Mohammad Tariqul Islam, Somaya Al Maadeed, Susu M Zughaier, Muhammad Salman Khan, et al · 2021
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Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
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Dataset distillation by matching training trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu · 2022
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The liver tumor segmentation benchmark (lits)
Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, et al · 2023
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Minimizing the accumulated trajectory error to improve dataset distillation
Jiawei Du, Yidi Jiang, Vincent YF Tan, Joey Tianyi Zhou, and Haizhou Li · 2023
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Sequential subset matching for dataset distillation
Jiawei Du, Qin Shi, and Joey Tianyi Zhou · 2023
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A survey on dataset distillation: Approaches, applications and future directions
Jiahui Geng, Zongxiong Chen, Yuandou Wang, Herbert Woisetschlaeger, Sonja Schimmler, Ruben Mayer, Zhiming Zhao, and Chunming Rong · 2023
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Towards lossless dataset distillation via difficulty-aligned trajectory matching
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Dc-bench: Dataset condensation benchmark
Justin Cui, Ruochen Wang, Si Si, and Cho-Jui Hsieh · 2022
Cited alongside, same era.
Compressed gastric image generation based on soft-label dataset distillation for medical data sharing
Guang Li, Ren Togo, Takahiro Ogawa, and Miki Haseyama · 2022
Cited alongside, same era.
Dataset distillation for medical dataset sharing
Guang Li, Ren Togo, Takahiro Ogawa, and Miki Haseyama · 2022
Cited alongside, same era.
Dataset distillation via factorization
Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, and Xinchao Wang · 2022
Cited alongside, same era.
A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Cafe: Learning to condense dataset by aligning features
Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Shuo Yang, Shuo Wang, Guan Huang, Hakan Bilen, Xinchao Wang, and Yang You · 2022
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Ziyao Guo, Kai Wang, George Cazenavette, Hui Li, Kaipeng Zhang, and Yang You · 2023
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A comprehensive survey to dataset distillation
Shiye Lei and Dacheng Tao · 2023
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Noveen Sachdeva and Julian McAuley · 2023
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Convnets match vision transformers at scale
Samuel L Smith, Andrew Brock, Leonard Berrada, and Soham De · 2023
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Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification
Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, and Bingbing Ni · 2023
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Dataset distillation: A comprehensive review
Ruonan Yu, Songhua Liu, and Xinchao Wang · 2023
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Dataset condensation with distribution matching
Bo Zhao and Hakan Bilen · 2023
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