@inproceedings{he2022masked,
  title={Masked autoencoders are scalable vision learners},
  author={He, Kaiming and Chen, Xinlei and Xie, Saining and Li, Yanghao and Doll{\'a}r, Piotr and Girshick, Ross},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={16000--16009},
  year={2022}
}

@article{rebuffi2017learning,
  title={Learning multiple visual domains with residual adapters},
  author={Rebuffi, Sylvestre-Alvise and Bilen, Hakan and Vedaldi, Andrea},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}


@inproceedings{rebuffi2018efficient,
  title={Efficient parametrization of multi-domain deep neural networks},
  author={Rebuffi, Sylvestre-Alvise and Bilen, Hakan and Vedaldi, Andrea},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={8119--8127},
  year={2018}
}

@inproceedings{li2022cross,
  title={Cross-domain few-shot learning with task-specific adapters},
  author={Li, Wei-Hong and Liu, Xialei and Bilen, Hakan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7161--7170},
  year={2022}
}

@article{triantafillou2019meta,
  title={Meta-dataset: A dataset of datasets for learning to learn from few examples},
  author={Triantafillou, Eleni and Zhu, Tyler and Dumoulin, Vincent and Lamblin, Pascal and Evci, Utku and Xu, Kelvin and Goroshin, Ross and Gelada, Carles and Swersky, Kevin and Manzagol, Pierre-Antoine and others},
  journal={arXiv preprint arXiv:1903.03096},
  year={2019}
}

@inproceedings{houlsby2019parameter,
  title={Parameter-efficient transfer learning for NLP},
  author={Houlsby, Neil and Giurgiu, Andrei and Jastrzebski, Stanislaw and Morrone, Bruna and De Laroussilhe, Quentin and Gesmundo, Andrea and Attariyan, Mona and Gelly, Sylvain},
  booktitle={International Conference on Machine Learning},
  pages={2790--2799},
  year={2019},
  organization={PMLR}
}

@inproceedings{he-etal-2022-sparseadapter,
    title = "{S}parse{A}dapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters",
    author = "He, Shwai  and
      Ding, Liang  and
      Dong, Daize  and
      Zhang, Jeremy  and
      Tao, Dacheng",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-emnlp.160",
    pages = "2184--2190",
    abstract = "Adapter Tuning, which freezes the pretrained language models (PLMs) and only fine-tunes a few extra modules, becomes an appealing efficient alternative to the full model fine-tuning. Although computationally efficient, the recent Adapters often increase parameters (e.g. bottleneck dimension) for matching the performance of full model fine-tuning, which we argue goes against their original intention. In this work, we re-examine the parameter-efficiency of Adapter through the lens of network pruning (we name such plug-in concept as SparseAdapter) and find that SparseAdapter can achieve comparable or better performance than standard Adapters when the sparse ratio reaches up to 80{\%}. Based on our findings, we introduce an easy but effective setting {``}Large-Sparse{''} to improve the model capacity of Adapters under the same parameter budget. Experiments on five competitive Adapters upon three advanced PLMs show that with proper sparse method (e.g. SNIP) and ratio (e.g. 40{\%}) SparseAdapter can consistently outperform their corresponding counterpart. Encouragingly, with the Large-Sparse setting, we can obtain further appealing gains, even outperforming the full fine-tuning by a large margin.",
}

@inproceedings{NEURIPS2021_081be9fd,
 author = {Karimi Mahabadi, Rabeeh and Henderson, James and Ruder, Sebastian},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
 pages = {1022--1035},
 publisher = {Curran Associates, Inc.},
 title = {Compacter: Efficient Low-Rank Hypercomplex Adapter Layers},
 url = {https://proceedings.neurips.cc/paper_files/paper/2021/file/081be9fdff07f3bc808f935906ef70c0-Paper.pdf},
 volume = {34},
 year = {2021}
}

@article{zhu2021counter,
  title={Counter-interference adapter for multilingual machine translation},
  author={Zhu, Yaoming and Feng, Jiangtao and Zhao, Chengqi and Wang, Mingxuan and Li, Lei},
  journal={arXiv preprint arXiv:2104.08154},
  year={2021}
}

@article{aghajanyan2020intrinsic,
  title={Intrinsic dimensionality explains the effectiveness of language model fine-tuning},
  author={Aghajanyan, Armen and Zettlemoyer, Luke and Gupta, Sonal},
  journal={arXiv preprint arXiv:2012.13255},
  year={2020}
}

@inproceedings{
hu2022lora,
title={Lo{RA}: Low-Rank Adaptation of Large Language Models},
author={Edward J Hu and yelong shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=nZeVKeeFYf9}
}

@article{edalati2022krona,
  title={KronA: Parameter Efficient Tuning with Kronecker Adapter},
  author={Edalati, Ali and Tahaei, Marzieh and Kobyzev, Ivan and Nia, Vahid Partovi and Clark, James J and Rezagholizadeh, Mehdi},
  journal={arXiv preprint arXiv:2212.10650},
  year={2022}
}

@article{lian2022scaling,
  title={Scaling \& shifting your features: A new baseline for efficient model tuning},
  author={Lian, Dongze and Zhou, Daquan and Feng, Jiashi and Wang, Xinchao},
  journal={arXiv preprint arXiv:2210.08823},
  year={2022}
}

@inproceedings{ben-zaken-etal-2022-bitfit,
    title = "{B}it{F}it: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models",
    author = "Ben Zaken, Elad  and
      Goldberg, Yoav  and
      Ravfogel, Shauli",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-short.1",
    doi = "10.18653/v1/2022.acl-short.1",
    pages = "1--9",
}

@inproceedings{VPT_jia,
author = {Jia, Menglin and Tang, Luming and Chen, Bor-Chun and Cardie, Claire and Belongie, Serge and Hariharan, Bharath and Lim, Ser-Nam},
title = {Visual Prompt Tuning},
year = {2022},
isbn = {978-3-031-19826-7},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-19827-4_41},
doi = {10.1007/978-3-031-19827-4_41},
booktitle = {Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXIII},
pages = {709–727},
numpages = {19},
location = {Tel Aviv, Israel}
}

@techreport{maji13fine-grained,
   title         = {Fine-Grained Visual Classification of Aircraft},
   author        = {S. Maji and J. Kannala and E. Rahtu
                    and M. Blaschko and A. Vedaldi},
   year          = {2013},
   archivePrefix = {arXiv},
   eprint        = {1306.5151},
   primaryClass  = "cs-cv",
}

@article{chen2022adaptformer,
  title={Adaptformer: Adapting vision transformers for scalable visual recognition},
  author={Chen, Shoufa and Ge, Chongjian and Tong, Zhan and Wang, Jiangliu and Song, Yibing and Wang, Jue and Luo, Ping},
  journal={arXiv preprint arXiv:2205.13535},
  year={2022}
}

@article{chen2022conv,
  title={Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets},
  author={Chen, Hao and Tao, Ran and Zhang, Han and Wang, Yidong and Ye, Wei and Wang, Jindong and Hu, Guosheng and Savvides, Marios},
  journal={arXiv preprint arXiv:2208.07463},
  year={2022}
}

@INPROCEEDINGS {convnext,
author = {Z. Liu and H. Mao and C. Wu and C. Feichtenhofer and T. Darrell and S. Xie},
booktitle = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
title = {A ConvNet for the 2020s},
year = {2022},
volume = {},
issn = {},
pages = {11966-11976},
keywords = {computer vision;image segmentation;visualization;computational modeling;scalability;semantics;transformers},
doi = {10.1109/CVPR52688.2022.01167},
url = {https://doi.ieeecomputersociety.org/10.1109/CVPR52688.2022.01167},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = {jun}
}

@article{jie2022fact,
  title={FacT: Factor-Tuning for Lightweight Adaptation on Vision Transformer},
  author={Jie, Shibo and Deng, Zhi-Hong},
  journal={arXiv preprint arXiv:2212.03145},
  year={2022}
}

@inproceedings{liu2020shape,
  title={Shape adaptor: A learnable resizing module},
  author={Liu, Shikun and Lin, Zhe and Wang, Yilin and Zhang, Jianming and Perazzi, Federico and Johns, Edward},
  booktitle={Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XII 16},
  pages={661--677},
  year={2020},
  organization={Springer}
}

@article{ma2024segment,
  title={Segment anything in medical images},
  author={Ma, Jun and He, Yuting and Li, Feifei and Han, Lin and You, Chenyu and Wang, Bo},
  journal={Nature Communications},
  volume={15},
  number={1},
  pages={654},
  year={2024},
  publisher={Nature Publishing Group UK London}
}

@article{singh2020dmenet,
  title={DMENet: diabetic macular edema diagnosis using hierarchical ensemble of CNNs},
  author={Singh, Rajeev Kumar and Gorantla, Rohan},
  journal={Plos one},
  volume={15},
  number={2},
  pages={e0220677},
  year={2020},
  publisher={Public Library of Science San Francisco, CA USA}
}

@inproceedings{hu2022kernel,
  title={Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks},
  author={Hu, Yuhuang and Liu, Shih-Chii},
  booktitle={2022 26th International Conference on Pattern Recognition (ICPR)},
  pages={2192--2198},
  year={2022},
  organization={IEEE}
}

@article{patacchiola2022contextual,
  title={Contextual squeeze-and-excitation for efficient few-shot image classification},
  author={Patacchiola, Massimiliano and Bronskill, John and Shysheya, Aliaksandra and Hofmann, Katja and Nowozin, Sebastian and Turner, Richard},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={36680--36692},
  year={2022}
}

@inproceedings{tinyTL,
 author = {Cai, Han and Gan, Chuang and Zhu, Ligeng and Han, Song},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {11285--11297},
 publisher = {Curran Associates, Inc.},
 title = {TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/81f7acabd411274fcf65ce2070ed568a-Paper.pdf},
 volume = {33},
 year = {2020}
}

@article{training_bn_only,
  publtype={informal},
  author={Jonathan Frankle and David J. Schwab and Ari S. Morcos},
  title={Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs.},
  year={2020},
  cdate={1577836800000},
  journal={CoRR},
  volume={abs/2003.00152},
  url={https://arxiv.org/abs/2003.00152}
}

@inproceedings{
dosovitskiy2021an,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=YicbFdNTTy}
}

@inproceedings{touvron2022three,
  title={Three things everyone should know about vision transformers},
  author={Touvron, Hugo and Cord, Matthieu and El-Nouby, Alaaeldin and Verbeek, Jakob and J{\'e}gou, Herv{\'e}},
  booktitle={Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XXIV},
  pages={497--515},
  year={2022},
  organization={Springer}
}

@article{basu2023strong,
  title={Strong Baselines for Parameter Efficient Few-Shot Fine-tuning},
  author={Basu, Samyadeep and Massiceti, Daniela and Hu, Shell Xu and Feizi, Soheil},
  journal={arXiv preprint arXiv:2304.01917},
  year={2023}
}

@article{fischer2022prompt,
  title={Prompt Tuning for Parameter-efficient Medical Image Segmentation},
  author={Fischer, Marc and Bartler, Alexander and Yang, Bin},
  journal={arXiv preprint arXiv:2211.09233},
  year={2022}
}

@article{li2023llavamed,
  title={Llava-med: Training a large language-and-vision assistant for biomedicine in one day},
  author={Li, Chunyuan and Wong, Cliff and Zhang, Sheng and Usuyama, Naoto and Liu, Haotian and Yang, Jianwei and Naumann, Tristan and Poon, Hoifung and Gao, Jianfeng},
  journal={arXiv preprint arXiv:2306.00890},
  year={2023}
}

@inproceedings{
dutt2024fairtune,
title={FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis},
author={Dutt, Raman and Bohdal, Ondrej and Tsaftaris, Sotirios A and Hospedales, Timothy},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=ArpwmicoYW}
}

@InProceedings{UNET,
author="Ronneberger, Olaf
and Fischer, Philipp
and Brox, Thomas",
editor="Navab, Nassir
and Hornegger, Joachim
and Wells, William M.
and Frangi, Alejandro F.",
title="U-Net: Convolutional Networks for Biomedical Image Segmentation",
booktitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="234--241",
}

@article{silva2023transductive,
  title={Transductive few-shot adapters for medical image segmentation},
  author={Silva-Rodr{\'\i}guez, Julio and Dolz, Jose and Ayed, Ismail Ben},
  journal={arXiv preprint arXiv:2303.17051},
  year={2023}
}

@article{kirillov2023segment,
  title={Segment anything},
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C and Lo, Wan-Yen and others},
  journal={arXiv preprint arXiv:2304.02643},
  year={2023}
}

@article{deng2023segment,
  title={Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging},
  author={Deng, Ruining and Cui, Can and Liu, Quan and Yao, Tianyuan and Remedios, Lucas W and Bao, Shunxing and Landman, Bennett A and Wheless, Lee E and Coburn, Lori A and Wilson, Keith T and others},
  journal={arXiv preprint arXiv:2304.04155},
  year={2023}
}

@article{wu2023medical,
  title={Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation},
  author={Wu, Junde and Fu, Rao and Fang, Huihui and Liu, Yuanpei and Wang, Zhaowei and Xu, Yanwu and Jin, Yueming and Arbel, Tal},
  journal={arXiv preprint arXiv:2304.12620},
  year={2023}
}

@article{mazurowski2023segment,
  title={Segment Anything Model for Medical Image Analysis: an Experimental Study},
  author={Mazurowski, Maciej A and Dong, Haoyu and Gu, Hanxue and Yang, Jichen and Konz, Nicholas and Zhang, Yixin},
  journal={arXiv preprint arXiv:2304.10517},
  year={2023}
}

@article{ho2020denoising,
  title={Denoising diffusion probabilistic models},
  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={6840--6851},
  year={2020}
}

@inproceedings{rombach2022high,
  title={High-resolution image synthesis with latent diffusion models},
  author={Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj{\"o}rn},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={10684--10695},
  year={2022}
}

@article{balaji2022ediffi,
  title={ediffi: Text-to-image diffusion models with an ensemble of expert denoisers},
  author={Balaji, Yogesh and Nah, Seungjun and Huang, Xun and Vahdat, Arash and Song, Jiaming and Kreis, Karsten and Aittala, Miika and Aila, Timo and Laine, Samuli and Catanzaro, Bryan and others},
  journal={arXiv preprint arXiv:2211.01324},
  year={2022}
}

@article{saharia2022photorealistic,
  title={Photorealistic text-to-image diffusion models with deep language understanding},
  author={Saharia, Chitwan and Chan, William and Saxena, Saurabh and Li, Lala and Whang, Jay and Denton, Emily L and Ghasemipour, Kamyar and Gontijo Lopes, Raphael and Karagol Ayan, Burcu and Salimans, Tim and others},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={36479--36494},
  year={2022}
}

@article{dhariwal2021diffusion,
  title={Diffusion models beat gans on image synthesis},
  author={Dhariwal, Prafulla and Nichol, Alexander},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={8780--8794},
  year={2021}
}

@article{kingma2021variational,
  title={Variational diffusion models},
  author={Kingma, Diederik and Salimans, Tim and Poole, Ben and Ho, Jonathan},
  journal={Advances in neural information processing systems},
  volume={34},
  pages={21696--21707},
  year={2021}
}

@article{xie2023difffit,
  title={DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning},
  author={Xie, Enze and Yao, Lewei and Shi, Han and Liu, Zhili and Zhou, Daquan and Liu, Zhaoqiang and Li, Jiawei and Li, Zhenguo},
  journal={arXiv preprint arXiv:2304.06648},
  year={2023}
}

@article{Peebles2022DiT,
  title={Scalable Diffusion Models with Transformers},
  author={William Peebles and Saining Xie},
  year={2022},
  journal={arXiv preprint arXiv:2212.09748},
}

@inproceedings{moon2022fine,
  title={Fine-tuning Diffusion Models with Limited Data},
  author={Moon, Taehong and Choi, Moonseok and Lee, Gayoung and Ha, Jung-Woo and Lee, Juho},
  booktitle={NeurIPS 2022 Workshop on Score-Based Methods}
}

@article{xiang2023closer,
  title={A Closer Look at Parameter-Efficient Tuning in Diffusion Models},
  author={Xiang, Chendong and Bao, Fan and Li, Chongxuan and Su, Hang and Zhu, Jun},
  journal={arXiv preprint arXiv:2303.18181},
  year={2023}
}

@inproceedings{lottery_ticket1,
  author       = {Jonathan Frankle and
                  Michael Carbin},
  title        = {The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks},
  booktitle    = {7th International Conference on Learning Representations, {ICLR} 2019,
                  New Orleans, LA, USA, May 6-9, 2019},
  publisher    = {OpenReview.net},
  year         = {2019},
  url          = {https://openreview.net/forum?id=rJl-b3RcF7},
  timestamp    = {Thu, 25 Jul 2019 13:03:15 +0200},
  biburl       = {https://dblp.org/rec/conf/iclr/FrankleC19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{NEURIPS2020_b6af2c97,
 author = {Chen, Tianlong and Frankle, Jonathan and Chang, Shiyu and Liu, Sijia and Zhang, Yang and Wang, Zhangyang and Carbin, Michael},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {15834--15846},
 publisher = {Curran Associates, Inc.},
 title = {The Lottery Ticket Hypothesis for Pre-trained BERT Networks},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/b6af2c9703f203a2794be03d443af2e3-Paper.pdf},
 volume = {33},
 year = {2020}
}

@inproceedings{prasanna-etal-2020-bert,
    title = "{W}hen {BERT} {P}lays the {L}ottery, {A}ll {T}ickets {A}re {W}inning",
    author = "Prasanna, Sai  and
      Rogers, Anna  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.259",
    doi = "10.18653/v1/2020.emnlp-main.259",
    pages = "3208--3229",
}

@INPROCEEDINGS{imagenet,
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Kai Li and Li Fei-Fei},
  booktitle={2009 IEEE Conference on Computer Vision and Pattern Recognition}, 
  title={ImageNet: A large-scale hierarchical image database}, 
  year={2009},
  volume={},
  number={},
  pages={248-255},
  doi={10.1109/CVPR.2009.5206848}}

@inproceedings{
ridnik2021imagenetk,
title={ImageNet-21K Pretraining for the Masses},
author={Tal Ridnik and Emanuel Ben-Baruch and Asaf Noy and Lihi Zelnik-Manor},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)},
year={2021},
url={https://openreview.net/forum?id=Zkj_VcZ6ol}
}

@article{brown2020language,
  title={Language models are few-shot learners},
  author={Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and others},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={1877--1901},
  year={2020}
}

@article{schick2020s,
  title={It's not just size that matters: Small language models are also few-shot learners},
  author={Schick, Timo and Sch{\"u}tze, Hinrich},
  journal={arXiv preprint arXiv:2009.07118},
  year={2020}
}

@article{lester2021power,
  title={The power of scale for parameter-efficient prompt tuning},
  author={Lester, Brian and Al-Rfou, Rami and Constant, Noah},
  journal={arXiv preprint arXiv:2104.08691},
  year={2021}
}

@article{li2021prefix,
  title={Prefix-tuning: Optimizing continuous prompts for generation},
  author={Li, Xiang Lisa and Liang, Percy},
  journal={arXiv preprint arXiv:2101.00190},
  year={2021}
}

@inproceedings{Qin2021ExploringUI,
  title={Exploring Universal Intrinsic Task Subspace via Prompt Tuning},
  author={Yujia Qin and Xiaozhi Wang and Yusheng Su and Yankai Lin and Ning Ding and Jing Yi and Weize Chen and Zhiyuan Liu and Juanzi Li and Lei Hou and Peng Li and Maosong Sun and Jie Zhou},
  year={2021}
}

@inproceedings{svladapterbmvc2022,
    author    = {Pantazis, Omiros and Brostow, Gabriel and Jones, Kate and Mac Aodha, Oisin},
    title     = {SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models},
    booktitle = {British Machine Vision Conference (BMVC)},
    year      = {2022}
}

@article{hinton2015distilling,
  title={Distilling the knowledge in a neural network},
  author={Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
  journal={arXiv preprint arXiv:1503.02531},
  year={2015}
}

@inproceedings{MolchanovTKAK17,
  author       = {Pavlo Molchanov and
                  Stephen Tyree and
                  Tero Karras and
                  Timo Aila and
                  Jan Kautz},
  title        = {Pruning Convolutional Neural Networks for Resource Efficient Inference},
  booktitle    = {5th International Conference on Learning Representations, {ICLR} 2017,
                  Toulon, France, April 24-26, 2017, Conference Track Proceedings},
  publisher    = {OpenReview.net},
  year         = {2017},
  url          = {https://openreview.net/forum?id=SJGCiw5gl},
  timestamp    = {Thu, 25 Jul 2019 14:25:44 +0200},
  biburl       = {https://dblp.org/rec/conf/iclr/MolchanovTKAK17.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{augasta2013pruning,
  title={Pruning algorithms of neural networks—a comparative study},
  author={Augasta, M and Kathirvalavakumar, Thangairulappan},
  journal={Open Computer Science},
  volume={3},
  number={3},
  pages={105--115},
  year={2013},
  publisher={De Gruyter Open Access}
}

@inproceedings{Zhang2021CanSS,
  title={Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?},
  author={Dinghuai Zhang and Kartik Ahuja and Yilun Xu and Yisen Wang and Aaron C. Courville},
  booktitle={International Conference on Machine Learning},
  year={2021}
}

@article{han2023svdiff,
  title={SVDiff: Compact Parameter Space for Diffusion Fine-Tuning},
  author={Han, Ligong and Li, Yinxiao and Zhang, Han and Milanfar, Peyman and Metaxas, Dimitris and Yang, Feng},
  journal={arXiv preprint arXiv:2303.11305},
  year={2023}
}

@article{Robb2020FewShotAO,
  title={Few-Shot Adaptation of Generative Adversarial Networks},
  author={Esther Robb and Wen-Sheng Chu and Abhishek Kumar and Jia-Bin Huang},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.11943}
}

@article{chambon2022adapting,
  title={Adapting pretrained vision-language foundational models to medical imaging domains},
  author={Chambon, Pierre and Bluethgen, Christian and Langlotz, Curtis P and Chaudhari, Akshay},
  journal={arXiv preprint arXiv:2210.04133},
  year={2022}
}

@article{al2020dataset,
  title={Dataset of breast ultrasound images},
  author={Al-Dhabyani, Walid and Gomaa, Mohammed and Khaled, Hussien and Fahmy, Aly},
  journal={Data in brief},
  volume={28},
  pages={104863},
  year={2020},
  publisher={Elsevier}
}

@article{HAM10000,
  title={The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions},
  author={Tschandl, Philipp and Rosendahl, Cliff and Kittler, Harald},
  journal={Scientific data},
  volume={5},
  number={1},
  pages={1--9},
  year={2018},
  publisher={Nature Publishing Group}
}

@inproceedings{groh2021evaluating,
  title={Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset},
  author={Groh, Matthew and Harris, Caleb and Soenksen, Luis and Lau, Felix and Han, Rachel and Kim, Aerin and Koochek, Arash and Badri, Omar},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={1820--1828},
  year={2021}
}

And the second paper:

@article{groh2022towards,
  title={Towards transparency in dermatology image datasets with skin tone annotations by experts, crowds, and an algorithm},
  author={Groh, Matthew and Harris, Caleb and Daneshjou, Roxana and Badri, Omar and Koochek, Arash},
  journal={arXiv preprint arXiv:2207.02942},
  year={2022}
}


@misc{riley_kiefer_2023, title={SMDG, A Standardized Fundus Glaucoma Dataset}, url={https://www.kaggle.com/ds/2329670}, DOI={10.34740/KAGGLE/DS/2329670}, publisher={Kaggle}, author={Riley Kiefer}, year={2023} }

@inproceedings{kiefer2022survey,
  title={A Survey of Glaucoma Detection Algorithms using Fundus and OCT Images},
  author={Kiefer, Riley and Steen, Jessica and Abid, Muhammad and Ardali, Mahsa R and Amjadian, Ehsan},
  booktitle={2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)},
  pages={0191--0196},
  year={2022},
  organization={IEEE}
}

@article{johnson2019mimic,
  title={MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports},
  author={Johnson, Alistair EW and Pollard, Tom J and Berkowitz, Seth J and Greenbaum, Nathaniel R and Lungren, Matthew P and Deng, Chih-ying and Mark, Roger G and Horng, Steven},
  journal={Scientific data},
  volume={6},
  number={1},
  pages={317},
  year={2019},
  publisher={Nature Publishing Group UK London}
}

@misc{diffusers,
  author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},
  title = {Diffusers: State-of-the-art diffusion models},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huggingface/diffusers}}
}

@Misc{peft,
  title =        {PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods},
  author =       {Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, Sayak Paul},
  howpublished = {\url{https://github.com/huggingface/peft}},
  year =         {2022}
}

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}

@article{paszke2019pytorch,
  title={Pytorch: An imperative style, high-performance deep learning library},
  author={Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and others},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@misc{PL,
author="Falcon, WA",
title="Pytorch lightning",
journal="GitHub",
year="2019",
volume="3",
URL="https://cir.nii.ac.jp/crid/1370013168774120069"
}

@article{liaw2018tune,
    title={Tune: A Research Platform for Distributed Model Selection and Training},
    author={Liaw, Richard and Liang, Eric and Nishihara, Robert
            and Moritz, Philipp and Gonzalez, Joseph E and Stoica, Ion},
    journal={arXiv preprint arXiv:1807.05118},
    year={2018}
}

@misc{kastryulin2022piq,
  title = {PyTorch Image Quality: Metrics for Image Quality Assessment},
  url = {https://arxiv.org/abs/2208.14818},
  author = {Kastryulin, Sergey and Zakirov, Jamil and Prokopenko, Denis and Dylov, Dmitry V.},
  doi = {10.48550/ARXIV.2208.14818},
  publisher = {arXiv},
  year = {2022}
}

@misc{piq,
  title={{PyTorch Image Quality}: Metrics and Measure for Image Quality Assessment},
  url={https://github.com/photosynthesis-team/piq},
  note={Open-source software available at https://github.com/photosynthesis-team/piq},
  author={Sergey Kastryulin and Dzhamil Zakirov and Denis Prokopenko},
  year={2019}
}

@inproceedings{huang2017densely,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4700--4708},
  year={2017}
}


@inproceedings{Cohen2022xrv,
title = {{TorchXRayVision: A library of chest X-ray datasets and models}},
author = {Cohen, Joseph Paul and Viviano, Joseph D. and Bertin, Paul and Morrison, Paul and Torabian, Parsa and Guarrera, Matteo and Lungren, Matthew P and Chaudhari, Akshay and Brooks, Rupert and Hashir, Mohammad and Bertrand, Hadrien},
booktitle = {Medical Imaging with Deep Learning},
url = {https://github.com/mlmed/torchxrayvision},
arxivId = {2111.00595},
year = {2022}
}

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

@article{RadImageNet,
author = {Mei, Xueyan and Liu, Zelong and Robson, Philip M. and Marinelli, Brett and Huang, Mingqian and Doshi, Amish and Jacobi, Adam and Cao, Chendi and Link, Katherine E. and Yang, Thomas and Wang, Ying and Greenspan, Hayit and Deyer, Timothy and Fayad, Zahi A. and Yang, Yang},
title = {RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning},
journal = {Radiology: Artificial Intelligence},
volume = {0},
number = {ja},
pages = {e210315},
year = {0},
doi = {10.1148/ryai.210315},

URL = { 
        https://doi.org/10.1148/ryai.210315
    
},
eprint = { 
        https://doi.org/10.1148/ryai.210315
}
}

@misc{
li2018massively,
title={Massively Parallel Hyperparameter Tuning},
author={Lisha Li and Kevin Jamieson and Afshin Rostamizadeh and Katya Gonina and Moritz Hardt and Benjamin Recht and Ameet Talwalkar},
year={2018},
url={https://openreview.net/forum?id=S1Y7OOlRZ},
}

@inproceedings{Imagenet21k,
 author = {Ridnik, Tal and Ben-Baruch, Emanuel and Noy, Asaf and Zelnik, Lihi},
 booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks},
 editor = {J. Vanschoren and S. Yeung},
 pages = {},
 publisher = {Curran},
 title = {ImageNet-21K Pretraining for the Masses},
 url = {https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/98f13708210194c475687be6106a3b84-Paper-round1.pdf},
 volume = {1},
 year = {2021}
}

@article{de2018clinically,
  title={Clinically applicable deep learning for diagnosis and referral in retinal disease},
  author={De Fauw, Jeffrey and Ledsam, Joseph R and Romera-Paredes, Bernardino and Nikolov, Stanislav and Tomasev, Nenad and Blackwell, Sam and Askham, Harry and Glorot, Xavier and O’Donoghue, Brendan and Visentin, Daniel and others},
  journal={Nature medicine},
  volume={24},
  number={9},
  pages={1342--1350},
  year={2018},
  publisher={Nature Publishing Group}
}

@article{esteva2017dermatologist,
  title={Dermatologist-level classification of skin cancer with deep neural networks},
  author={Esteva, Andre and Kuprel, Brett and Novoa, Roberto A and Ko, Justin and Swetter, Susan M and Blau, Helen M and Thrun, Sebastian},
  journal={nature},
  volume={542},
  number={7639},
  pages={115--118},
  year={2017},
  publisher={Nature Publishing Group}
}

@article{mckinney2020international,
  title={International evaluation of an AI system for breast cancer screening},
  author={McKinney, Scott Mayer and Sieniek, Marcin and Godbole, Varun and Godwin, Jonathan and Antropova, Natasha and Ashrafian, Hutan and Back, Trevor and Chesus, Mary and Corrado, Greg S and Darzi, Ara and others},
  journal={Nature},
  volume={577},
  number={7788},
  pages={89--94},
  year={2020},
  publisher={Nature Publishing Group UK London}
}

@article{zhou2021review,
  title={A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises},
  author={Zhou, S Kevin and Greenspan, Hayit and Davatzikos, Christos and Duncan, James S and Van Ginneken, Bram and Madabhushi, Anant and Prince, Jerry L and Rueckert, Daniel and Summers, Ronald M},
  journal={Proceedings of the IEEE},
  volume={109},
  number={5},
  pages={820--838},
  year={2021},
  publisher={IEEE}
}

@article{willemink2020preparing,
  title={Preparing medical imaging data for machine learning},
  author={Willemink, Martin J and Koszek, Wojciech A and Hardell, Cailin and Wu, Jie and Fleischmann, Dominik and Harvey, Hugh and Folio, Les R and Summers, Ronald M and Rubin, Daniel L and Lungren, Matthew P},
  journal={Radiology},
  volume={295},
  number={1},
  pages={4--15},
  year={2020},
  publisher={Radiological Society of North America}
}

@article{azizi2022selfSupMedical,
  title={Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging},
  author={Azizi, Shekoofeh and Culp, Laura and Freyberg, Jan and Mustafa, Basil and Baur, Sebastien and Kornblith, Simon and Chen, Ting and Tomasev, Nenad and Mitrovi{\'c}, Jovana and Strachan, Patricia and others},
  journal={Nature Biomedical Engineering},
  pages={1--24},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@InProceedings{azizi2021bigMedicalSSRL,
  author    = {Shekoofeh Azizi and Basil Mustafa and Fiona Ryan and Zachary Beaver and Jan Freyberg and Jonathan Deaton and Aaron Loh and Alan Karthikesalingam and Simon Kornblith and Ting Chen and Vivek Natarajan and Mohammad Norouzi},
  booktitle = {ICCV},
  title     = {Big Self-Supervised Models Advance Medical Image Classification},
  year      = {2021},
}

@article{hong2020trends,
  title={Trends in diagnostic imaging utilization among medicare and commercially insured adults from 2003 through 2016},
  author={Hong, Arthur S and Levin, David and Parker, Laurence and Rao, Vijay M and Ross-Degnan, Dennis and Wharam, J Frank},
  journal={Radiology},
  volume={294},
  number={2},
  pages={342--350},
  year={2020},
  publisher={Radiological Society of North America}
}

@Article{huang2023selfMedical,
  author       = {Shih-Cheng Huang and Anuj Pareek and Malte Jensen and Matthew P. Lungren and Serena Yeung and Akshay S. Chaudhari},
  date         = {2023},
  journaltitle = {NPJ Digital Medicine},
  title        = {Self-supervised learning for medical image classification: a systematic review and implementation guidelines},
}

@Article{bommasani2021foundation,
  author   = {Bommasani, Rishi and others},
  title    = {On the opportunities and risks of foundation models},
  journal  = {arXiv:2108.07258},
  keywords = {big picture},
  year     = {2021},
}

@InProceedings{gouk2020distance,
  author    = {Gouk, Henry and Hospedales, Timothy and others},
  booktitle = {International Conference on Learning Representations},
  title     = {Distance-Based Regularisation of Deep Networks for Fine-Tuning},
  year      = {2020},
}

@inproceedings{xuhong2018explicit,
  title={Explicit inductive bias for transfer learning with convolutional networks},
  author={Xuhong, LI and Grandvalet, Yves and Davoine, Franck},
  booktitle={International Conference on Machine Learning},
  pages={2825--2834},
  year={2018},
  organization={PMLR}
}

@InProceedings{he2022towards,
  author    = {He, Junxian and Zhou, Chunting and Ma, Xuezhe and Berg-Kirkpatrick, Taylor and Neubig, Graham},
  booktitle = {International Conference on Learning Representations},
  title     = {Towards a Unified View of Parameter-Efficient Transfer Learning},
  year      = {2022},
}

@misc{chambon2022roentgen,
      title={RoentGen: Vision-Language Foundation Model for Chest X-ray Generation}, 
      author={Pierre Chambon and Christian Bluethgen and Jean-Benoit Delbrouck and Rogier Van der Sluijs and Małgorzata Połacin and Juan Manuel Zambrano Chaves and Tanishq Mathew Abraham and Shivanshu Purohit and Curtis P. Langlotz and Akshay Chaudhari},
      year={2022},
      eprint={2211.12737},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@article{dutt2022automatic,
  title={Automatic localization and brand detection of cervical spine hardware on radiographs using weakly supervised machine learning},
  author={Dutt, Raman and Mendonca, Dylan and Phen, Huai Ming and Broida, Samuel and Ghassemi, Marzyeh and Gichoya, Judy and Banerjee, Imon and Yoon, Tim and Trivedi, Hari},
  journal={Radiology: Artificial Intelligence},
  volume={4},
  number={2},
  pages={e210099},
  year={2022},
  publisher={Radiological Society of North America}
}

@inproceedings{yii2022rethinking,
  title={Rethinking Retinal Image Quality: Treating Quality Threshold as a Tunable Hyperparameter},
  author={Yii, Fabian SL and Dutt, Raman and MacGillivray, Tom and Dhillon, Baljean and Bernabeu, Miguel and Strang, Niall},
  booktitle={Ophthalmic Medical Image Analysis: 9th International Workshop, OMIA 2022, Held in Conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings},
  pages={73--83},
  year={2022},
  organization={Springer}
}

@article{zhang2023customized,
  title={Customized Segment Anything Model for Medical Image Segmentation},
  author={Zhang, Kaidong and Liu, Dong},
  journal={arXiv preprint arXiv:2304.13785},
  year={2023}
}

@misc{rsna2018,
  author = {Radiological Society of North America},
  title = {RSNA Pneumonia Detection Dataset},
  howpublished = {Available at: \url{https://www.kaggle.com/competitions/rsna-pneumonia-detection-challenge/data}},
  year = {2018},
  note = {Accessed: October, 2022}
}

@inproceedings{radford2021learning,
  title={Learning transferable visual models from natural language supervision},
  author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
  booktitle={International conference on machine learning},
  pages={8748--8763},
  year={2021},
  organization={PMLR}
}

@article{loshchilov2017decoupled,
  title={Decoupled weight decay regularization},
  author={Loshchilov, Ilya and Hutter, Frank},
  journal={arXiv preprint arXiv:1711.05101},
  year={2017}
}

@inproceedings{akiba2019optuna,
  title={Optuna: A next-generation hyperparameter optimization framework},
  author={Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori},
  booktitle={Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery \& data mining},
  pages={2623--2631},
  year={2019}
}