@String(PAMI = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV = {Int. J. Comput. Vis.})
@String(CVPR= {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV= {Int. Conf. Comput. Vis.})
@String(ECCV= {Eur. Conf. Comput. Vis.})
@String(NIPS= {Adv. Neural Inform. Process. Syst.})
@String(ICPR = {Int. Conf. Pattern Recog.})
@String(BMVC= {Brit. Mach. Vis. Conf.})
@String(TOG= {ACM Trans. Graph.})
@String(TIP  = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TMM  = {IEEE Trans. Multimedia})
@String(ACMMM= {ACM Int. Conf. Multimedia})
@String(ICME = {Int. Conf. Multimedia and Expo})
@String(ICASSP= {ICASSP})
@String(ICIP = {IEEE Int. Conf. Image Process.})
@String(ACCV  = {ACCV})
@String(ICLR = {Int. Conf. Learn. Represent.})
@String(IJCAI = {IJCAI})
@String(PR   = {Pattern Recognition})
@String(AAAI = {AAAI})
@String(CVPRW= {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@String(SPL = {IEEE Sign. Process. Letters})
@String(VR   = {Vis. Res.})
@String(JOV  = {J. Vis.})
@String(TVC  = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF  = {Comput. Graph. Forum})
@String(CVM = {Computational Visual Media})

@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NIPS  = {NeurIPS})
@String(ICPR  = {ICPR})
@String(BMVC  = {BMVC})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   = {IEEE TMM})
@String(ACMMM = {ACM MM})
@String(ICME  = {ICME})
@String(ICASSP= {ICASSP})
@String(ICIP  = {ICIP})
@String(ACCV  = {ACCV})
@String(ICLR  = {ICLR})
@String(IJCAI = {IJCAI})
@String(PR = {PR})
@String(AAAI = {AAAI})
@String(CVPRW= {CVPRW})
@String(CSVT = {IEEE TCSVT})

@misc{lc_1,
author = {Bray, Freddie and Ferlay, Jacques and Soerjomataram, Isabelle and Siegel, Rebecca L. and Torre, Lindsey A. and Jemal, Ahmedin},
title = {Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries},
journal = {CA: A Cancer Journal for Clinicians},
volume = {68},
number = {6},
pages = {394-424},
keywords = {cancer, epidemiology, incidence, survival},
doi = {https://doi.org/10.3322/caac.21492},
url = {https://acsjournals.onlinelibrary.wiley.com/doi/abs/10.3322/caac.21492},
eprint = {https://acsjournals.onlinelibrary.wiley.com/doi/pdf/10.3322/caac.21492},
abstract = {Abstract This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6\% of the total cases) and the leading cause of cancer death (18.4\% of the total cancer deaths), closely followed by female breast cancer (11.6\%), prostate cancer (7.1\%), and colorectal cancer (6.1\%) for incidence and colorectal cancer (9.2\%), stomach cancer (8.2\%), and liver cancer (8.2\%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. {\copyright} 2018 American Cancer Society},
year = {2018}
}

@misc{unknown-author-no-date,
	title = {{stabilityai/sd-vae-ft-ema · Hugging Face}},
	url = {https://huggingface.co/stabilityai/sd-vae-ft-ema},
}

@article{Rogers2010,
  author    = {Rogers, T. K. and Muir, K. and Baragwanath, P. and Evans, R. and Hodgson, J. and Kelly, M. and Morris, J. and Shepherd, S. and Smith, E. and Stephenson, T. and Welch, L. and Wilkinson, S.},
  title     = {Primary care radiography in the early diagnosis of lung cancer: randomised controlled trial},
  journal   = {The British Journal of General Practice},
  year      = {2010},
  volume    = {60},
  number    = {571},
  pages     = {e315--e322},
  doi       = {10.3399/bjgp10X514729},
  url       = {https://pmc.ncbi.nlm.nih.gov/articles/PMC2842172/}
}

@article{Sogancioglu2024NODE21,
  author    = {Ecem Sogancioglu and Bram van Ginneken and Finn Behrendt and Marcel Bengs and Alexander Schlaefer and Miron Radu and Di Xu and Ke Sheng and Fabien Scalzo and Eric Marcus and Samuele Papa and Jonas Teuwen and Ernst Th. Scholten and Steven Schalekamp and Nils Hendrix and Colin Jacobs and Ward Hendrix and Clara I. S\'anchez and Keelin Murphy},
  title     = {Nodule Detection and Generation on Chest X-rays: NODE21 Challenge},
  journal   = {IEEE Transactions on Medical Imaging},
  year      = {2024},
  volume    = {43},
  number    = {8},
  pages     = {2839--2853},
  doi       = {10.1109/TMI.2024},
  url       = {https://arxiv.org/abs/2401.02192}
}

@inproceedings{Kang2020ContraGAN,
  author    = {Minguk Kang and Jaesik Park},
  title     = {Contrastive Learning for Conditional Image Generation (ContraGAN)},
  booktitle = {Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020)},
  year      = {2020},
  pages     = {2685--2696},
  url       = {https://arxiv.org/abs/2006.12681},
  doi       = {10.48550/arXiv.2006.12681}
}

@inproceedings{Ho2020DDPM,
  author    = {Jonathan Ho and Ajay Jain and Pieter Abbeel},
  title     = {Denoising Diffusion Probabilistic Models},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year      = {2020}
}

@inproceedings{Dhariwal2021Improved,
  author    = {Prafulla Dhariwal and Alexander Nichol},
  title     = {Diffusion Models Beat {GAN}s on Image Synthesis},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year      = {2021}
}

@inproceedings{Rombach2022LDM,
  author    = {Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Bj{\"o}rn Ommer},
  title     = {High-Resolution Image Synthesis with Latent Diffusion Models},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2022},
  pages     = {10684--10695}
}

@misc{prabhakar2024lorasoupsmergingloras,
      title={LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks}, 
      author={Akshara Prabhakar and Yuanzhi Li and Karthik Narasimhan and Sham Kakade and Eran Malach and Samy Jelassi},
      year={2024},
      eprint={2410.13025},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.13025}, 
}

@misc{StableDiffusion2022,
  author       = {CompVis and Stability AI and Runway},
  title        = {Stable Diffusion},
  year         = {2022},
  howpublished = {\url{https://github.com/CompVis/stable-diffusion}}
}

@article{Nichol2022GLIDE,
  author    = {Alex Nichol and Prafulla Dhariwal and Aditya Ramesh and Pranav Shyam and Pamela Mishkin and Bob McGrew and Ilya Sutskever and Mark Chen},
  title     = {GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models},
  journal   = {arXiv preprint arXiv:2112.10741},
  year      = {2022}
}

@inproceedings{Saharia2022Imagen,
  author    = {Chitwan Saharia and William Chan and Saurabh Saxena and Lala Li and Jay Whang and Emily Denton and Seyed Kamyar Seyed Ghasemipour and Burcu Karagol Ayan and Sharan Narang and Shixiang Shane Gu and Tim Salimans and Jonathan Ho and David Fleet and Mohammad Norouzi},
  title     = {Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year      = {2022}
}

@misc{StableDiffusion2022,
  author       = {CompVis and Stability AI and Runway},
  title        = {Stable Diffusion},
  year         = {2022},
  howpublished = {\url{https://github.com/CompVis/stable-diffusion}}
}

@misc{kong2024loraswitchboostingefficiencydynamic,
      title={LoRA-Switch: Boosting the Efficiency of Dynamic LLM Adapters via System-Algorithm Co-design}, 
      author={Rui Kong and Qiyang Li and Xinyu Fang and Qingtian Feng and Qingfeng He and Yazhu Dong and Weijun Wang and Yuanchun Li and Linghe Kong and Yunxin Liu},
      year={2024},
      eprint={2405.17741},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2405.17741}, 
}

@misc{zhou2018unetnestedunetarchitecture,
      title={UNet++: A Nested U-Net Architecture for Medical Image Segmentation}, 
      author={Zongwei Zhou and Md Mahfuzur Rahman Siddiquee and Nima Tajbakhsh and Jianming Liang},
      year={2018},
      eprint={1807.10165},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1807.10165}, 
}

@misc{seer_csr_1975_2014,
  editor    = {Howlader, N. and Noone, A. M. and Krapcho, M. and Miller, D. and Bishop, K. and Kosary, C. L. and Yu, M. and Ruhl, J. and Tatalovich, Z. and Mariotto, A. and Lewis, D. R. and Chen, H. S. and Feuer, E. J. and Cronin, K. A.},
  title     = {SEER Cancer Statistics Review, 1975-2014},
  publisher = {National Cancer Institute},
  address   = {Bethesda, MD},
  year      = {2017},
  url       = {https://seer.cancer.gov/csr/1975_2014/},
  note      = {Based on November 2016 SEER data submission, posted to the SEER web site, April 2017}
}

@misc{liu2021swintransformerhierarchicalvision,
      title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows}, 
      author={Ze Liu and Yutong Lin and Yue Cao and Han Hu and Yixuan Wei and Zheng Zhang and Stephen Lin and Baining Guo},
      year={2021},
      eprint={2103.14030},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2103.14030}, 
}

@article{Balagurunathan2019,
  author    = {Balagurunathan, Y. and Chen, A. and Clark, K. and et al.},
  title     = {Quantitative Imaging Features Improve Discrimination of Pulmonary Nodules},
  journal   = {Scientific Reports},
  year      = {2019},
  volume    = {9},
  pages     = {44562},
  doi       = {10.1038/s41598-019-44562-z}
}

@misc{nlst2011,
  author       = {National Lung Screening Trial Research Team},
  title        = {Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening},
  howpublished = {\emph{New England Journal of Medicine}, 365(5):395--409},
  year         = {2011},
  doi          = {10.1056/NEJMoa1102873},
  url          = {https://www.nejm.org/doi/full/10.1056/NEJMoa1102873},
  note         = {Accessed November 30, 2025},
  abstract     = {The National Lung Screening Trial investigators report that persons undergoing three annual screening examinations with low-dose computed tomography had a 20\% reduction in lung-cancer mortality compared with those screened with annual chest radiography.}
}

@misc{lc_5,
author = {Irvin, Jeremy and Rajpurkar, Pranav and Ko, Michael and Yu, Yifan and Ciurea-Ilcus, Silviana and Chute, Chris and Marklund, Henrik and Haghgoo, Behzad and Ball, Robyn and Shpanskaya, Katie and Seekins, Jayne and Mong, David A. and Halabi, Safwan S. and Sandberg, Jesse K. and Jones, Ricky and Larson, David B. and Langlotz, Curtis P. and Patel, Bhavik N. and Lungren, Matthew P. and Ng, Andrew Y.},
title = {CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison},
year = {2019},
isbn = {978-1-57735-809-1},
publisher = {AAAI Press},
url = {https://doi.org/10.1609/aaai.v33i01.3301590},
doi = {10.1609/aaai.v33i01.3301590},
abstract = {Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models.1},
booktitle = {Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence},
articleno = {73},
numpages = {8},
location = {Honolulu, Hawaii, USA},
series = {AAAI'19/IAAI'19/EAAI'19}
}

@article{lc_7,
  author    = {Khan, Ali Nawaz and Al-Jahdali, H. H. and Allen, C. M. and Irion, K. L. and Al Ghanem, S. and Koteyar, S. S.},
  title     = {The calcified lung nodule: What does it mean?},
  journal   = {Annals of Thoracic Medicine},
  volume    = {5},
  number    = {2},
  pages     = {67-79},
  year      = {2010},
  doi       = {10.4103/1817-1737.62469}
}

@article{lc_8,
  author    = {Zhang, R. and Tian, P. and Qiu, Z. and Liang, Y. and Li, W.},
  title     = {The growth feature and its diagnostic value for benign and malignant pulmonary nodules met in routine clinical practice},
  journal   = {Journal of Thoracic Disease},
  volume    = {12},
  number    = {5},
  pages     = {2019--2030},
  year      = {2020},
  doi       = {10.21037/jtd-19-3591},
  url       = {https://doi.org/10.21037/jtd-19-3591}
}

@inproceedings{odena2017acgan,
  title={Conditional image synthesis with auxiliary classifier gans},
  author={Odena, Augustus and Olah, Christopher and Shlens, Jonathon},
  booktitle={Proceedings of the 34th International Conference on Machine Learning},
  pages={2642--2651},
  year={2017},
  organization={PMLR}
}

@inproceedings{lee2021reacgan,
  title={ReACGAN: Training Auxiliary Classifier GANs with a Reversely Architected Discriminator},
  author={Lee, Younggeun and Ahn, Namhyuk and Kim, Daijin},
  booktitle={Advances in Neural Information Processing Systems},
  volume={34},
  pages={20049--20060},
  year={2021}
}

@inproceedings{zhao2021crfill,
  title={Large Scale Image Completion via Co-Modulated Generative Adversarial Networks},
  author={Zhao, Shengyu and Liu, Hongyu and Lin, Jiaya and Zhu, Zhe and Loy, Chen Change},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={12838--12847},
  year={2021}
}

@article{schultheiss2021,
  author    = {Schultheiss, M. and Sogancioglu, E. and Setio, A. and Schwyzer, M. and Candemir, S.},
  title     = {The nodule generation challenge: synthetic nodules in chest radiographs},
  journal   = {Scientific Reports},
  year      = {2021},
  volume    = {11},
  number    = {1},
  pages     = {16165},
  doi       = {10.1038/s41598-021-94750-z},
  url       = {https://doi.org/10.1038/s41598-021-94750-z}
}

@article{hanaoka2024,
  author    = {Hanaoka, Shunsuke and Nomura, Yusuke and Yoshikawa, Takashi and Nakao, Takuya and Takenaga, Takashi and Matsuzaki, Hiroshi and Yamamichi, Nobutake and Abe, Osamu},
  title     = {Detection of pulmonary nodules in chest radiographs: novel cost function for effective network training with purely synthesized datasets},
  journal   = {International Journal of Computer Assisted Radiology and Surgery},
  year      = {2024},
  volume    = {19},
  number    = {10},
  pages     = {1991--2000},
  doi       = {10.1007/s11548-024-03227-7},
  pmid      = {39003437},
  pmcid     = {PMC11442563},
  month     = {Oct},
  note      = {Epub 2024 Jul 13}
}

@article{wang2022synthetic,
  author    = {Wang, Shuo and Qiao, Yifan and Qian, Wei and Yang, Ying and Shi, Jun},
  title     = {Generation of synthetic ground glass nodules using generative adversarial networks},
  journal   = {European Radiology Experimental},
  year      = {2022},
  volume    = {6},
  number    = {1},
  pages     = {32},
  doi       = {10.1186/s41747-022-00311-y},
  url       = {https://doi.org/10.1186/s41747-022-00311-y}
}

@article{albert2009solitary,
  author    = {Albert, Richard H. and Russell, John J.},
  title     = {Evaluation of the solitary pulmonary nodule},
  journal   = {American Family Physician},
  year      = {2009},
  volume    = {80},
  number    = {8},
  pages     = {827--831},
  month     = {Oct},
  issn      = {0002-838X},
  pmid      = {19835344}
}

@misc{gandikota-2023,
	author = {Gandikota, Rohit and Materzynska, Joanna and Zhou, Tingrui and Torralba, Antonio and Bau, David},
	month = {11},
	title = {{Concept Sliders: LORA adaptors for precise control in diffusion models}},
	year = {2023},
	url = {https://arxiv.org/abs/2311.12092},
}

@inproceedings{dhariwal2021diffusion,
  title     = {Diffusion Models Beat GANs on Image Synthesis},
  author    = {Dhariwal, Prafulla and Nichol, Alexander Quinn},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2021},
  url       = {https://proceedings.neurips.cc/paper/2021/hash/49ad23d1ec9fa4bd8d77d02681df5cfa-Abstract.html}
}

@article{mou2023t2iadapter,
  title   = {T2I-Adapters: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models},
  author  = {Mou, Chong and Zhang, Lvmin and Rao, Anyi and Agrawala, Maneesh},
  journal = {arXiv preprint arXiv:2302.08453},
  year    = {2023},
  url     = {https://arxiv.org/abs/2302.08453}
}

@article{hertz2022prompt,
  title   = {Prompt-to-Prompt Image Editing with Cross Attention Control},
  author  = {Hertz, Amir and Mokady, Ron and Tenenbaum, Jonathan and Aberman, Kfir and Cohen-Or, Daniel and Pritch, Yael},
  journal = {arXiv preprint arXiv:2208.01626},
  year    = {2022},
  url     = {https://arxiv.org/abs/2208.01626}
}

@article{podell2023sdxl,
  title   = {SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis},
  author  = {Podell, David and others},
  journal = {arXiv preprint arXiv:2307.01952},
  year    = {2023},
  url     = {https://arxiv.org/abs/2307.01952}
}

@article{zhang2023controlnet,
  title   = {Adding Conditional Control to Text-to-Image Diffusion Models},
  author  = {Zhang, Lvmin and Rao, Anyi and Agrawala, Maneesh},
  journal = {arXiv preprint arXiv:2302.05543},
  year    = {2023},
  url     = {https://arxiv.org/abs/2302.05543}
}

@article{yang2022survey,
  title   = {A Survey on Generative Diffusion Models},
  author  = {Yang, Ling and Zhang, Zhilong and Hong, Sihyeon and Xu, Wenqi and Zhao, Zhongjie and Li, Bo},
  journal = {arXiv preprint arXiv:2209.02646},
  year    = {2022},
  url     = {https://arxiv.org/abs/2209.02646}
}

@inproceedings{hu2021lora,
  title     = {LoRA: Low-Rank Adaptation of Large Language Models},
  author    = {Hu, Edward J. and Shen, Yelong and Wallis, Phillip and others},
  booktitle = {International Conference on Learning Representations},
  year      = {2021},
  url       = {https://arxiv.org/abs/2106.09685}
}

@article{ruiz2023dreambooth,
  title   = {DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation},
  author  = {Ruiz, Nataniel and Li, Yuanzhen and Jampani, Varun and others},
  journal = {arXiv preprint arXiv:2208.12242},
  year    = {2023},
  url     = {https://arxiv.org/abs/2208.12242}
}

@article{lora-diffusion-2023,
  title   = {LORA for Stable Diffusion: Low-Rank Adaptation for Fast Personalization},
  author  = {Koh, Jing Yu and others},
  journal = {arXiv preprint arXiv:2311.13435},
  year    = {2023},
  url     = {https://arxiv.org/abs/2311.13435}
}

@article{lyu2023lowrank,
  title   = {Low-Rank Adaptation for Fast Text-to-Image Diffusion Fine-Tuning},
  author  = {Lyu, Xueyan and others},
  journal = {arXiv preprint arXiv:2312.06632},
  year    = {2023},
  url     = {https://arxiv.org/abs/2312.06632}
}

@article{kufel-2023,
	author = {Kufel, Jakub and Biel{\'o}wka, Micha{\l} and Rojek, Marcin and Mitr{\k e}ga, Adam and Lewandowski, Piotr and Cebula, Maciej and Krawczyk, Dariusz and Biel{\'o}wka, Marta and Kondo{\l}, Dominika and Bargie{\l}-{\L}\k{a}czek, Katarzyna and Paszkiewicz, Iga and Czogalik, {\L}ukasz and Kaczy{\'n}ska, Dominika and Woc{\l}aw, Aleksandra and Gruszczy{\'n}ska, Katarzyna and Nawrat, Zbigniew},
	journal = {Journal of Personalized Medicine},
	month = {9},
	number = {10},
	pages = {1426},
	title = {{Multi-Label classification of chest x-ray abnormalities using transfer learning techniques}},
	volume = {13},
	year = {2023},
	doi = {10.3390/jpm13101426},
	url = {https://doi.org/10.3390/jpm13101426},
}

@article{shiraishi1996computer,
  title={Computer-aided diagnosis in medical imaging: operating characteristics and observer performance studies},
  author={Shiraishi, Junji and Luther, Gordon and Metz, Charles E and Doi, Kunio},
  journal={Journal of the Japanese Society of Radiological Technology},
  volume={52},
  number={9},
  pages={1184--1191},
  year={1996}
}

@misc{zhong2024multiloracompositionimagegeneration,
      title={Multi-LoRA Composition for Image Generation}, 
      author={Ming Zhong and Yelong Shen and Shuohang Wang and Yadong Lu and Yizhu Jiao and Siru Ouyang and Donghan Yu and Jiawei Han and Weizhu Chen},
      year={2024},
      eprint={2402.16843},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2402.16843}, 
}

@misc{peebles-2022,
	author = {Peebles, William and Xie, Saining},
	month = {12},
	title = {{Scalable Diffusion Models with Transformers}},
	year = {2022},
	url = {https://arxiv.org/abs/2212.09748},
}

@misc{shah2023ziplorasubjectstyleeffectively,
      title={ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs}, 
      author={Viraj Shah and Nataniel Ruiz and Forrester Cole and Erika Lu and Svetlana Lazebnik and Yuanzhen Li and Varun Jampani},
      year={2023},
      eprint={2311.13600},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2311.13600}, 
}

@misc{ouyang2025kloraunlockingtrainingfreefusion,
      title={K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAs}, 
      author={Ziheng Ouyang and Zhen Li and Qibin Hou},
      year={2025},
      eprint={2502.18461},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
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@misc{unknown-author-2019,
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@misc{unknown-author-2021,
	title = {{Multi-Scale conditional Generative adversarial network for Small-Sized lung nodules using class activation region influence maximization}},
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@article{behrendt2023systematic,
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  url     = {https://doi.org/10.1038/s41598-023-37270-2}
}

@article{Nakayama1952Orthogonality,
  author    = {Tadasi Nakayama},
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@misc{zheng2025decoupleorthogonalizedatafreeframework,
      title={Decouple and Orthogonalize: A Data-Free Framework for LoRA Merging}, 
      author={Shenghe Zheng and Hongzhi Wang and Chenyu Huang and Xiaohui Wang and Tao Chen and Jiayuan Fan and Shuyue Hu and Peng Ye},
      year={2025},
      eprint={2505.15875},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.15875}, 
}

@misc{ho2022classifierfreediffusionguidance,
      title={Classifier-Free Diffusion Guidance}, 
      author={Jonathan Ho and Tim Salimans},
      year={2022},
      eprint={2207.12598},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2207.12598}, 
}

@article{peebles-2023,
	author = {Peebles, William and Xie, Saining},
	journal = {2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
	month = {10},
	title = {{Scalable Diffusion Models with Transformers}},
	year = {2023},
	doi = {10.1109/iccv51070.2023.00387},
	url = {https://doi.org/10.1109/iccv51070.2023.00387},
}

@misc{zhang2025lorireducingcrosstaskinterference,
      title={LoRI: Reducing Cross-Task Interference in Multi-Task Low-Rank Adaptation}, 
      author={Juzheng Zhang and Jiacheng You and Ashwinee Panda and Tom Goldstein},
      year={2025},
      eprint={2504.07448},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2504.07448}, 
}

@article{ann-2021,
	author = {Ann, Kyeongjin and Jang, Yeonggul and Shim, Hackjoon and Chang, Hyuk-Jae},
	journal = {IEEE Access},
	month = {1},
	pages = {139426--139437},
	title = {{Multi-Scale conditional Generative adversarial network for Small-Sized lung nodules using class activation region influence maximization}},
	volume = {9},
	year = {2021},
	doi = {10.1109/access.2021.3116034},
	url = {https://doi.org/10.1109/access.2021.3116034},
}

@misc{gu2023mixofshowdecentralizedlowrankadaptation,
      title={Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models}, 
      author={Yuchao Gu and Xintao Wang and Jay Zhangjie Wu and Yujun Shi and Yunpeng Chen and Zihan Fan and Wuyou Xiao and Rui Zhao and Shuning Chang and Weijia Wu and Yixiao Ge and Ying Shan and Mike Zheng Shou},
      year={2023},
      eprint={2305.18292},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2305.18292}, 
}