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The popularity of large-scale pre-training has promoted the development of medical foundation models.
Multitask learning: A knowledge-based source of inductive bias1
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On the opportunities and risks of foundation models
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Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun · 2018
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The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Philipp Tschandl, Cliff Rosendahl, and Harald Kittler · 2018
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Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D Lawrence, and Zhenwen Dai · 2019
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Shikun Liu, Edward Johns, and Andrew J Davison · 2019
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Sindy Löwe, Peter O’Connor, and Bastiaan S Veeling · 2019
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Wonpyo Park, Dongju Kim, Yan Lu, and Minsu Cho · 2019
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Yu Zhang and Qiang Yang · 2021
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Mitigating gradient bias in multi-objective learning: A provably convergent approach
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Factorizing knowledge in neural networks
Xingyi Yang, Jingwen Ye, and Xinchao Wang · 2022
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Risk of bias in chest radiography deep learning foundation models
Ben Glocker, Charles Jones, Mélanie Roschewitz, and Stefan Winzeck · 2023
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Segment anything model for medical images?
Yuhao Huang, Xin Yang, Lian Liu, Han Zhou, Ao Chang, Xinrui Zhou, Rusi Chen, Junxuan Yu, Jiongquan Chen, Chaoyu Chen, et al · 2023
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Independent component alignment for multi-task learning
Dmitry Senushkin, Nikolay Patakin, Arseny Kuznetsov, and Anton Konushin · 2023
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Batched low-rank adaptation of foundation models
Yeming Wen and Swarat Chaudhuri · 2023
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Chaoyi Wu, Jiayu Lei, Qiaoyu Zheng, Weike Zhao, Weixiong Lin, Xiaoman Zhang, Xiao Zhou, Ziheng Zhao, Ya Zhang, Yanfeng Wang, et al · 2023
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Lora-fa: Memory-efficient low-rank adaptation for large language models fine-tuning
Longteng Zhang, Lin Zhang, Shaohuai Shi, Xiaowen Chu, and Bo Li · 2023
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