@ARTICLE{cat1,
  title    = "Cataract surgery practice patterns worldwide: a survey",
  author   = "Rossi, Tommaso and Romano, Mario R and Iannetta, Danilo and
              Romano, Vito and Gualdi, Luca and D'Agostino, Isabella and
              Ripandelli, Guido",
  journal  = "BMJ Open Ophthalmol",
  volume   =  6,
  number   =  1,
  pages    = "e000464",
  month    =  jan,
  year     =  2021,
  address  = "England",
  keywords = "epidemiology; lens and zonules; public health; treatment surgery",
  language = "en"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{cat2,
  title    = "Geographic Variation in the Rate and Timing of Cataract Surgery
              Among {US} Communities",
  author   = "Kauh, Courtney Y and Blachley, Taylor S and Lichter, Paul R and
              Lee, Paul P and Stein, Joshua D",
  journal  = "JAMA Ophthalmol",
  volume   =  134,
  number   =  3,
  pages    = "267--276",
  month    =  mar,
  year     =  2016,
  address  = "United States",
  language = "en"
}

@article{skill1,
title = "Video-based assessment of intraoperative surgical skill",
keywords = "Cataract surgery, Deep learning, Surgical skill, Video-based assessment",
author = "Sanchit Hira and Digvijay Singh and Kim, {Tae Soo} and Shobhit Gupta and Gregory Hager and Shameema Sikder and Vedula, {S. Swaroop}",
year = "2022",
month = oct,
language = "English (US)",
volume = "17",
pages = "1801--1811",
journal = "Computer-Assisted Radiology and Surgery",
issn = "1861-6410",
publisher = "Springer Verlag",
number = "10",
}

@article{skill2,
title = "Objective assessment of intraoperative technical skill in capsulorhexis using videos of cataract surgery",
keywords = "Capsulorhexis, Cataract surgery, Crowdsourcing, Deep learning, Neural networks, Surgical skill assessment, Tool trajectories",
author = "Kim, {Tae Soo} and Molly O{\textquoteright}Brien and Sidra Zafar and Hager, {Gregory D.} and Shameema Sikder and Vedula, {S. Swaroop}",
year = "2019",
month = jun,
day = "1",
language = "English (US)",
volume = "14",
pages = "1097--1105",
journal = "Computer-Assisted Radiology and Surgery",
issn = "1861-6410",
publisher = "Springer Verlag",
number = "6",
}

@InProceedings{glsformer,
author="Shah, Nisarg A.
and Sikder, Shameema
and Vedula, S. Swaroop
and Patel, Vishal M.",
editor="Greenspan, Hayit
and Madabhushi, Anant
and Mousavi, Parvin
and Salcudean, Septimiu
and Duncan, James
and Syeda-Mahmood, Tanveer
and Taylor, Russell",
title="GLSFormer: Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="386--396"
}

@inproceedings{barlow,
  title={Cross-Dataset Adaptation for Instrument Classification in Cataract Surgery Videos},
  author={Jay N. Paranjape and Shameema Sikder and Vishal M. Patel and S. Swaroop Vedula},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2023},
  url={https://api.semanticscholar.org/CorpusID:260704213}
}

@Article{cat1k,
author={Ghamsarian, Negin
and El-Shabrawi, Yosuf
and Nasirihaghighi, Sahar
and Putzgruber-Adamitsch, Doris
and Zinkernagel, Martin
and Wolf, Sebastian
and Schoeffmann, Klaus
and Sznitman, Raphael},
title={Cataract-1K Dataset for Deep-Learning-Assisted Analysis of Cataract Surgery Videos},
journal={Scientific Data},
year={2024},
month={Apr},
day={12},
volume={11},
number={1},
pages={373},
url={https://doi.org/10.1038/s41597-024-03193-4}
}

@misc{cataractbot,
author = {Ramjee, Pragnya and Sachdeva, Bhuvan and Golechha, Satvik and Kulkarni, Shreyas and Fulari, Geeta and Murali, Dr. Kaushik and Jain, Mohit},
title = {CataractBot: An LLM-Powered Expert-in-the-Loop Chatbot for Cataract Patients},
howpublished = {arXiv},
year = {2024},
month = {February},
url = {https://www.microsoft.com/en-us/research/publication/cataractbot-an-llm-powered-expert-in-the-loop-chatbot-for-cataract-patients/},
}

@inproceedings{vtimellm,
  title={Vtimellm: Empower llm to grasp video moments},
  author={Huang, Bin and Wang, Xin and Chen, Hong and Song, Zihan and Zhu, Wenwu},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14271--14280},
  year={2024}
}

@inproceedings{videochatgpt,
    title={Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models},
    author={Maaz, Muhammad and Rasheed, Hanoona and Khan, Salman and Khan, Fahad Shahbaz},
    booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024)},
    year={2024}
}

@inproceedings{videomae,
  title={Video{MAE}: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training},
  author={Zhan Tong and Yibing Song and Jue Wang and Limin Wang},
  booktitle={Advances in Neural Information Processing Systems},
  year={2022}
}

@inproceedings{cataracts101,
author = {Schoeffmann, Klaus and Taschwer, Mario and Sarny, Stephanie and M\"{u}nzer, Bernd and Primus, Manfred J\"{u}rgen and Putzgruber, Doris},
title = {Cataract-101: video dataset of 101 cataract surgeries},
year = {2018},
isbn = {9781450351928},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3204949.3208137},
booktitle = {Proceedings of the 9th ACM Multimedia Systems Conference},
pages = {421–425},
numpages = {5},
keywords = {surgical actions, ophthalmology, multimedia information systems, medical multimedia, medical data sets},
location = {Amsterdam, Netherlands},
series = {MMSys '18}
}

@misc{clip,
      title={Learning Transferable Visual Models From Natural Language Supervision}, 
      author={Alec Radford and Jong Wook Kim and Chris Hallacy and Aditya Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
      year={2021},
      eprint={2103.00020},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2103.00020}, 
}

@misc{vicuna,
    title = {Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90\%* ChatGPT Quality},
    url = {https://lmsys.org/blog/2023-03-30-vicuna/},
    author = {Chiang, Wei-Lin and Li, Zhuohan and Lin, Zi and Sheng, Ying and Wu, Zhanghao and Zhang, Hao and Zheng, Lianmin and Zhuang, Siyuan and Zhuang, Yonghao and Gonzalez, Joseph E. and Stoica, Ion and Xing, Eric P.},
    month = {March},
    year = {2023}
}

@inproceedings{
lora,
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{tong2022videomae,
  title={Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training},
  author={Tong, Zhan and Song, Yibing and Wang, Jue and Wang, Limin},
  journal={Advances in neural information processing systems},
  volume={35},
  pages={10078--10093},
  year={2022}
}

@article{shah2025mathsf,
  title={CSMAE: Cataract Surgical Masked Autoencoder (MAE) based Pre-training},
  author={Shah, Nisarg A and Bandara, Wele Gedara Chaminda and Skider, Shameema and Vedula, S Swaroop and Patel, Vishal M},
  journal={arXiv preprint arXiv:2502.08822},
  year={2025}
}

@article{banegas2024enhancing,
  title={Enhancing pre-service teachers’ projective agency for diverse and multilingual classrooms through a course on curriculum development},
  author={Banegas, Dar{\'\i}o Luis and Budzenski, Michael and Yang, Fang},
  journal={International Multilingual Research Journal},
  volume={18},
  number={3},
  pages={274--289},
  year={2024},
  publisher={Taylor \& Francis}
}

@inproceedings{shah2023gated,
  title={GLSFormer : Gated-Long, Short Sequence Transformer for Step Recognition in Surgical Videos},
  author={Shah, Nisarg A and Sikder, Shameema and Vedula, S Swaroop and Patel, Vishal M},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={386--396},
  year={2023},
  organization={Springer}
}

@inproceedings{gao2021trans,
  title={Trans-svnet: Accurate phase recognition from surgical videos via hybrid embedding aggregation transformer},
  author={Gao, Xiaojie and Jin, Yueming and Long, Yonghao and Dou, Qi and Heng, Pheng-Ann},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2021: 24th International Conference, Strasbourg, France, September 27--October 1, 2021, Proceedings, Part IV 24},
  pages={593--603},
  year={2021},
  organization={Springer}
}

@article{Ren2023TimeChat,
  title={TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding},
  author={Shuhuai Ren and Linli Yao and Shicheng Li and Xu Sun and Lu Hou},
  journal={ArXiv},
  year={2023},
  volume={abs/2312.02051},
}

@inproceedings{tascon2023localized,
title={Localized Questions in Medical Visual Question Answering},
author={Tascon-Morales, Sergio and M{'a}rquez-Neila, Pablo and Sznitman,Raphael},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={--},
year={2023},
organization={Springer} }

@article{medvidqa,
  author    = {Gupta, Deepak and Attal, Kush and Demner{-}Fushman, Dina},
  title     = {A dataset for medical instructional video classification and question answering},
  journal   = {Scientific Data},
  year      = {2023},
  volume    = {10},
  number    = {1},
  pages     = {158},
  doi       = {10.1038/s41597-023-02036-y},
  url       = {https://doi.org/10.1038/s41597-023-02036-y},
  abstract  = {This paper introduces a new challenge and datasets to foster research toward designing systems that can understand medical videos and provide visual answers to natural language questions. We believe medical videos may provide the best possible answers to many first aid, medical emergency, and medical education questions. Toward this, we created the MedVidCL and MedVidQA datasets and introduce the tasks of Medical Video Classification (MVC) and Medical Visual Answer Localization (MVAL), two tasks that focus on cross-modal (medical language and medical video) understanding. The proposed tasks and datasets have the potential to support the development of sophisticated downstream applications that can benefit the public and medical practitioners. Our datasets consist of 6,117 fine-grained annotated videos for the MVC task and 3,010 questions and answers timestamps from 899 videos for the MVAL task. These datasets have been verified and corrected by medical informatics experts. We have also benchmarked each task with the created MedVidCL and MedVidQA datasets and propose the multimodal learning methods that set competitive baselines for future research.},
  issn      = {2052-4463}
}

@misc{surgpubvideo,
      title={SurgPub-Video: A Comprehensive Surgical Video Dataset for Enhanced Surgical Intelligence in Vision-Language Model}, 
      author={Yaoqian Li and Xikai Yang and Dunyuan Xu and Yang Yu and Litao Zhao and Xiaowei Hu and Jinpeng Li and Pheng-Ann Heng},
      year={2025},
      eprint={2508.10054},
      archivePrefix={arXiv},
      primaryClass={q-bio.OT},
      url={https://arxiv.org/abs/2508.10054}, 
}

@article{surgicalVQA,
  author    = {Yuan, Kun and Kattel, Manasi and Lavanchy, Jo{\"e}l L. and Navab, Nassir and Srivastav, Vinkle and Padoy, Nicolas},
  title     = {Advancing surgical VQA with scene graph knowledge},
  journal   = {International Journal of Computer Assisted Radiology and Surgery},
  year      = {2024},
  volume    = {19},
  number    = {7},
  pages     = {1409--1417},
  doi       = {10.1007/s11548-024-03141-y},
  url       = {https://doi.org/10.1007/s11548-024-03141-y},
  abstract  = {The modern operating room is becoming increasingly complex, requiring innovative intra-operative support systems. While the focus of surgical data science has largely been on video analysis, integrating surgical computer vision with natural language capabilities is emerging as a necessity. Our work aims to advance visual question answering (VQA) in the surgical context with scene graph knowledge, addressing two main challenges in the current surgical VQA systems: removing question--condition bias in the surgical VQA dataset and incorporating scene-aware reasoning in the surgical VQA model design.},
  issn      = {1861-6429}
}

@misc{MedicalGPT,
  title={MedicalGPT: Training Medical GPT Model},
  author={Ming Xu},
  year={2023},
  howpublished={\url{https://github.com/shibing624/MedicalGPT}},
}