@article{batic2024endovit,
  title={EndoViT: pretraining vision transformers on a large collection of endoscopic images},
  author={Bati{\'c}, Dominik and Holm, Felix and {\"O}zsoy, Ege and Czempiel, Tobias and Navab, Nassir},
  journal={International Journal of Computer Assisted Radiology and Surgery},
  volume={19},
  number={6},
  pages={1085--1091},
  year={2024},
  publisher={Springer}
}

@misc{funke2023metricsmattersurgicalphase,
	title={Metrics Matter in Surgical Phase Recognition}, 
	author={Isabel Funke and Dominik Rivoir and Stefanie Speidel},
	year={2023},
	eprint={2305.13961},
	archivePrefix={arXiv},
	primaryClass={cs.CV},
	url={https://arxiv.org/abs/2305.13961}, 
}

@article{maier2017surgical,
  title={Surgical data science for next-generation interventions},
  author={Maier-Hein, Lena and Vedula, Swaroop S and Speidel, Stefanie and Navab, Nassir and Kikinis, Ron and Park, Adrian and Eisenmann, Matthias and Feussner, Hubertus and Forestier, Germain and Giannarou, Stamatia and others},
  journal={Nature Biomedical Engineering},
  volume={1},
  number={9},
  pages={691--696},
  year={2017},
  publisher={Nature Publishing Group}
}

@article{MAIERHEIN2022102306,
title = {Surgical data science -- from concepts toward clinical translation},
journal = {Medical Image Analysis},
volume = {76},
pages = {102306},
year = {2022},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2021.102306},
url = {https://www.sciencedirect.com/science/article/pii/S1361841521003510},
author = {Lena Maier-Hein and Matthias Eisenmann and Duygu Sarikaya and Keno M{\"{a}}rz and Toby Collins and Anand Malpani and Johannes Fallert and Hubertus Feussner and Stamatia Giannarou and Pietro Mascagni and Hirenkumar Nakawala and Adrian Park and Carla Pugh and Danail Stoyanov and Swaroop S. Vedula and Kevin Cleary and Gabor Fichtinger and Germain Forestier and Bernard Gibaud and Teodor Grantcharov and Makoto Hashizume and Doreen Heckmann-N{\"{o}}tzel and Hannes G. Kenngott and Ron Kikinis and Lars M{\"{u}}ndermann and Nassir Navab and Sinan Onogur and Tobias Ro{\ss} and Raphael Sznitman and Russell H. Taylor and Minu D. Tizabi and Martin Wagner and Gregory D. Hager and Thomas Neumuth and Nicolas Padoy and Justin Collins and Ines Gockel and Jan Goedeke and Daniel A. Hashimoto and Luc Joyeux and Kyle Lam and Daniel R. Leff and Amin Madani and Hani J. Marcus and Ozanan Meireles and Alexander Seitel and Dogu Teber and Frank {\"{U}}ckert and Beat P. M{\"{u}}ller-Stich and Pierre Jannin and Stefanie Speidel},
keywords = {Surgical data science, Artificial intelligence, Deep learning, Computer aided surgery, Clinical translation},
abstract = {Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.}
}


@article{ye2023heterogeneous,
  title={Heterogeneous federated learning: State-of-the-art and research challenges},
  author={Ye, Mang and Fang, Xiuwen and Du, Bo and Yuen, Pong C and Tao, Dacheng},
  journal={ACM Computing Surveys},
  volume={56},
  number={3},
  pages={1--44},
  year={2023},
  publisher={ACM New York, NY, USA}
}

@inproceedings{mcmahan2017communication,
  title={Communication-efficient learning of deep networks from decentralized data},
  author={McMahan, Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and y Arcas, Blaise Aguera},
  booktitle={Artificial intelligence and statistics},
  pages={1273--1282},
  year={2017},
  organization={PMLR}
}

@inproceedings{caldarola2022improving,
  title={Improving generalization in federated learning by seeking flat minima},
  author={Caldarola, Debora and Caputo, Barbara and Ciccone, Marco},
  booktitle={European Conference on Computer Vision},
  pages={654--672},
  year={2022},
  organization={Springer}
}

@article{twinanda2016endonet,
  title={Endonet: a deep architecture for recognition tasks on laparoscopic videos},
  author={Twinanda, Andru P and Shehata, Sherif and Mutter, Didier and Marescaux, Jacques and De Mathelin, Michel and Padoy, Nicolas},
  journal={IEEE transactions on medical imaging},
  volume={36},
  number={1},
  pages={86--97},
  year={2016},
  publisher={IEEE}
}

@article{nwoye2022rendezvous,
  title={Rendezvous: Attention mechanisms for the recognition of surgical action triplets in endoscopic videos},
  author={Nwoye, Chinedu Innocent and Yu, Tong and Gonzalez, Cristians and Seeliger, Barbara and Mascagni, Pietro and Mutter, Didier and Marescaux, Jacques and Padoy, Nicolas},
  journal={Medical Image Analysis},
  volume={78},
  pages={102433},
  year={2022},
  publisher={Elsevier}
}

@article{hong2020cholecseg8k,
  title={Cholecseg8k: a semantic segmentation dataset for laparoscopic cholecystectomy based on cholec80},
  author={Hong, W-Y and Kao, C-L and Kuo, Y-H and Wang, J-R and Chang, W-L and Shih, C-S},
  journal={arXiv preprint arXiv:2012.12453},
  year={2020}
}

@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{yang2023fedmae,
  title={FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder},
  author={Yang, Nan and Chen, Xuanyu and Liu, Charles Z and Yuan, Dong and Bao, Wei and Cui, Lizhen},
  journal={arXiv preprint arXiv:2303.11339},
  year={2023}
}

@article{izmailov2018averaging,
  title={Averaging weights leads to wider optima and better generalization},
  author={Izmailov, Pavel and Podoprikhin, Dmitrii and Garipov, Timur and Vetrov, Dmitry and Wilson, Andrew Gordon},
  journal={arXiv preprint arXiv:1803.05407},
  year={2018}
}

@article{beutel2020flower,
  title={Flower: A Friendly Federated Learning Research Framework},
  author={Beutel, Daniel J and Topal, Taner and Mathur, Akhil and Qiu, Xinchi and Fernandez-Marques, Javier and Gao, Yan and Sani, Lorenzo and Kwing, Hei Li and Parcollet, Titouan and Gusm{\~{a}}o, Pedro PB de and Lane, Nicholas D},
  journal={arXiv preprint arXiv:2007.14390},
  year={2020}
}

@inproceedings{ranftl2021vision,
  title={Vision transformers for dense prediction},
  author={Ranftl, Ren{\'e} and Bochkovskiy, Alexey and Koltun, Vladlen},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={12179--12188},
  year={2021}
}

@inproceedings{czempiel2020tecno,
  title={Tecno: Surgical phase recognition with multi-stage temporal convolutional networks},
  author={Czempiel, Tobias and Paschali, Magdalini and Keicher, Matthias and Simson, Walter and Feussner, Hubertus and Kim, Seong Tae and Navab, Nassir},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2020: 23rd International Conference, Lima, Peru, October 4--8, 2020, Proceedings, Part III 23},
  pages={343--352},
  year={2020},
  organization={Springer}
}

@article{nwoye2022data,
  title={Data splits and metrics for method benchmarking on surgical action triplet datasets},
  author={Nwoye, Chinedu Innocent and Padoy, Nicolas},
  journal={arXiv preprint arXiv:2204.05235},
  year={2022}
}

@article{bommasani2021opportunities,
  title={On the opportunities and risks of foundation models},
  author={Bommasani, Rishi and Hudson, Drew A and Adeli, Ehsan and Altman, Russ and Arora, Simran and von Arx, Sydney and Bernstein, Michael S and Bohg, Jeannette and Bosselut, Antoine and Brunskill, Emma and others},
  journal={arXiv preprint arXiv:2108.07258},
  year={2021}
}


@article{wang_fedkim_2025,
  title={Fedkim: Adaptive federated knowledge injection into medical foundation models},
  author={Wang, Xiaochen and Wang, Jiaqi and Xiao, Houping and Chen, Jinghui and Ma, Fenglong},
  journal={arXiv preprint arXiv:2408.10276},
  year={2024}
}


@misc{babendererde_federated-continual_2025,
	title = {Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity},
	url = {http://arxiv.org/abs/2501.04588},
	doi = {10.48550/arXiv.2501.04588},
	abstract = {Federated- and Continual Learning have been established as approaches to enable privacy-aware learning on continuously changing data, as required for deploying {AI} systems in histopathology images. However, data shifts can occur in a dynamic world, spatially between institutions and temporally, due to changing data over time. This leads to two issues: Client Drift, where the central model degrades from aggregating data from clients trained on shifted data, and Catastrophic Forgetting, from temporal shifts such as changes in patient populations. Both tend to degrade the model's performance of previously seen data or spatially distributed training. Despite both problems arising from the same underlying problem of data shifts, existing research addresses them only individually. In this work, we introduce a method that can jointly alleviate Client Drift and Catastrophic Forgetting by using our proposed Dynamic Barlow Continuity that evaluates client updates on a public reference dataset and uses this to guide the training process to a spatially and temporally shift-invariant model. We evaluate our approach on the histopathology datasets {BCSS} and Semicol and prove our method to be highly effective by jointly improving the dice score as much as from 15.8\% to 71.6\% in Client Drift and from 42.5\% to 62.8\% in Catastrophic Forgetting. This enables Dynamic Learning by establishing spatio-temporal shift-invariance.},
	number = {{arXiv}:2501.04588},
	publisher = {{arXiv}},
	author = {Babendererde, Niklas and Zhu, Haozhe and Fuchs, Moritz and Stieber, Jonathan and Mukhopadhyay, Anirban},
	urldate = {2026-01-22},
	date = {2025-01-08},
	eprinttype = {arxiv},
	eprint = {2501.04588 [cs]},
	note = {version: 1},
    year = {2025},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
	file = {Full Text PDF:/home/kirchnema/Zotero/storage/WFC3GHHV/Babendererde et al. - 2025 - Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity.pdf:application/pdf;Snapshot:/home/kirchnema/Zotero/storage/RKIUUCR5/2501.html:text/html},
}


@article{Kassem_2023,
   title={Federated Cycling (FedCy): Semi-Supervised Federated Learning of Surgical Phases},
   volume={42},
   ISSN={1558-254X},
   url={http://dx.doi.org/10.1109/TMI.2022.3222126},
   DOI={10.1109/tmi.2022.3222126},
   number={7},
   journal={IEEE Transactions on Medical Imaging},
   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
   author={Kassem, Hasan and Alapatt, Deepak and Mascagni, Pietro and Karargyris, Alexandros and Padoy, Nicolas},
   year={2023},
   month=jul, pages={1920--1931} }
   
@article{jogan2024quality,
	title={Quality over quantity? The role of data quality and uncertainty for AI in surgery},
	author={Jogan, Matja{\v{z}} and Kurada, Sruthi and Vasisht, Shubha and Singh, Vivek and Hashimoto, Daniel A},
	journal={Global Surgical Education-Journal of the Association for Surgical Education},
	volume={3},
	number={1},
	pages={79},
	year={2024},
	publisher={Springer}
}

@article{rabbani2024can,
	title={Can surgical computer vision benefit from large-scale visual foundation models?},
	author={Rabbani, Navid and Bartoli, Adrien},
	journal={International Journal of Computer Assisted Radiology and Surgery},
	volume={19},
	number={6},
	pages={1157--1163},
	year={2024},
	publisher={Springer}
}

@article{fu2024frozen,
	title={Frozen-DETR: Enhancing DETR with Image Understanding from Frozen Foundation Models},
	author={Fu, Shenghao and Yan, Junkai and Yang, Qize and Wei, Xihan and Xie, Xiaohua and Zheng, Wei-Shi},
	journal={arXiv preprint arXiv:2410.19635},
	year={2024}
}

@misc{chen2024frozensegharmonizingfrozenfoundation,
	title={FrozenSeg: Harmonizing Frozen Foundation Models for Open-Vocabulary Segmentation}, 
	author={Xi Chen and Haosen Yang and Sheng Jin and Xiatian Zhu and Hongxun Yao},
	year={2024},
	eprint={2409.03525},
	archivePrefix={arXiv},
	primaryClass={cs.CV},
	url={https://arxiv.org/abs/2409.03525}, 
}


@article{tolle2025real,
  title={Real world federated learning with a knowledge distilled transformer for cardiac CT imaging},
  author={T{\"o}lle, Malte and Garthe, Philipp and Scherer, Clemens and Seliger, Jan Moritz and Leha, Andreas and Kr{\"u}ger, Nina and Simm, Stefan and Martin, Simon and Eble, Sebastian and Kelm, Halvar and others},
  journal={npj Digital Medicine},
  volume={8},
  number={1},
  pages={88},
  year={2025},
  publisher={Nature Publishing Group UK London}
}

@misc{eckhoff_2024_15051218,
  author       = {Eckhoff, Jennifer and
                  Meireles, Ozanan and
                  Padoy, Nicolas and
                  Hashimoto, Daniel and
                  Alapatt, Deepak and
                  Rosman, Guy and
                  Mascagni, Pietro and
                  Mazellier, Jean-Paul and
                  Li, Xiang and
                  Lyu, Zhiliang and
                  Choksi, Sarah and
                  Filicori, Filippo and
                  Ban, Yutong},
  title        = {MICCAI 2025 Lighthouse Challenge: Society of
                   American Gastrointestinal and Endoscopic Surgeons
                   Critical View of Safety (SAGES-CVS)
                  },
  month        = oct,
  year         = 2024,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.15051218},
  url          = {https://doi.org/10.5281/zenodo.15051218},
}


@article{ramesh_dissecting_2023,
	title = {Dissecting self-supervised learning methods for surgical computer vision},
	volume = {88},
	issn = {1361-8415},
	url = {https://www.sciencedirect.com/science/article/pii/S1361841523001044},
	doi = {10.1016/j.media.2023.102844},
	abstract = {The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fully-supervised approaches for training such models require vast amounts of annotated data, imposing a prohibitively high cost; especially in the clinical domain. Self-Supervised Learning ({SSL}) methods, which have begun to gain traction in the general computer vision community, represent a potential solution to these annotation costs, allowing to learn useful representations from only unlabeled data. Still, the effectiveness of {SSL} methods in more complex and impactful domains, such as medicine and surgery, remains limited and unexplored. In this work, we address this critical need by investigating four state-of-the-art {SSL} methods ({MoCo} v2, {SimCLR}, {DINO}, {SwAV}) in the context of surgical computer vision. We present an extensive analysis of the performance of these methods on the Cholec80 dataset for two fundamental and popular tasks in surgical context understanding, phase recognition and tool presence detection. We examine their parameterization, then their behavior with respect to training data quantities in semi-supervised settings. Correct transfer of these methods to surgery, as described and conducted in this work, leads to substantial performance gains over generic uses of {SSL} -- up to 7.4\% on phase recognition and 20\% on tool presence detection -- as well as state-of-the-art semi-supervised phase recognition approaches by up to 14\%. Further results obtained on a highly diverse selection of surgical datasets exhibit strong generalization properties. The code is available at https://github.com/{CAMMA}-public/{SelfSupSurg}.},
	pages = {102844},
    journal = {Medical Image Analysis},
	journaltitle = {Medical Image Analysis},
	shortjournal = {Medical Image Analysis},
	author = {Ramesh, Sanat and Srivastav, Vinkle and Alapatt, Deepak and Yu, Tong and Murali, Aditya and Sestini, Luca and Nwoye, Chinedu Innocent and Hamoud, Idris and Sharma, Saurav and Fleurentin, Antoine and Exarchakis, Georgios and Karargyris, Alexandros and Padoy, Nicolas},
	urldate = {2025-09-15},
	date = {2023-08-01},
	keywords = {Deep learning, Endoscopic videos, Laparoscopic cholecystectomy, Self-supervised learning, Semi-supervised learning, Surgical computer vision},
	file = {ScienceDirect Full Text PDF:/home/kirchnema/Zotero/storage/ASXDVVLI/Ramesh et al. - 2023 - Dissecting self-supervised learning methods for surgical computer vision.pdf:application/pdf;ScienceDirect Snapshot:/home/kirchnema/Zotero/storage/2CAGADTK/S1361841523001044.html:text/html},
    year = 2023
}


@article{yuan_learning_2025,
	title = {Learning multi-modal representations by watching hundreds of surgical video lectures},
	volume = {105},
	issn = {1361-8415},
	url = {https://www.sciencedirect.com/science/article/pii/S1361841525001914},
	doi = {10.1016/j.media.2025.103644},
	abstract = {Recent advancements in surgical computer vision applications have been driven by vision-only models, which do not explicitly integrate the rich semantics of language into their design. These methods rely on manually annotated surgical videos to predict a fixed set of object categories, limiting their generalizability to unseen surgical procedures and downstream tasks. In this work, we put forward the idea that the surgical video lectures available through open surgical e-learning platforms can provide effective vision and language supervisory signals for multi-modal representation learning without relying on manual annotations. We address the surgery-specific linguistic challenges present in surgical video lectures by employing multiple complementary automatic speech recognition systems to generate text transcriptions. We then present a novel method, {SurgVLP} --- Surgical Vision Language Pre-training, for multi-modal representation learning. {SurgVLP} constructs a new contrastive learning objective to align video clip embeddings with the corresponding multiple text embeddings by bringing them together within a joint latent space. To effectively demonstrate the representational capability of the learned joint latent space, we introduce several vision-and-language surgical tasks and evaluate various vision-only tasks specific to surgery, e.g., surgical tool, phase, and triplet recognition. Extensive experiments across diverse surgical procedures and tasks demonstrate that the multi-modal representations learned by {SurgVLP} exhibit strong transferability and adaptability in surgical video analysis. Furthermore, our zero-shot evaluations highlight {SurgVLP}'s potential as a general-purpose foundation model for surgical workflow analysis, reducing the reliance on extensive manual annotations for downstream tasks, and facilitating adaptation methods such as few-shot learning to build a scalable and data-efficient solution for various downstream surgical applications. The code is available at https://github.com/{CAMMA}-public/{SurgVLP}.},
	pages = {103644},
	journaltitle = {Medical Image Analysis},
	shortjournal = {Medical Image Analysis},
	author = {Yuan, Kun and Srivastav, Vinkle and Yu, Tong and Lavanchy, Jo{\"{e}}l L. and Marescaux, Jacques and Mascagni, Pietro and Navab, Nassir and Padoy, Nicolas},
	urldate = {2025-09-15},
	date = {2025-10-01},
	keywords = {Multi-modal representation learning, Self-supervision, Surgical video lectures, Vision-and-language},
	file = {ScienceDirect Snapshot:/home/kirchnema/Zotero/storage/G9HHB6GF/S1361841525001914.html:text/html},
    year = 2025,
}


@misc{maier-hein_heidelberg_2021,
	title = {Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room},
	url = {http://arxiv.org/abs/2005.03501},
	doi = {10.48550/arXiv.2005.03501},
	abstract = {Image-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the proposed methods still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal ({HeiCo}) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on method robustness and generalization capabilities. Our data set comprises 30 laparoscopic videos and corresponding sensor data from medical devices in the operating room for three different types of laparoscopic surgery. Annotations include surgical phase labels for all video frames as well as information on instrument presence and corresponding instance-wise segmentation masks for surgical instruments (if any) in more than 10,000 individual frames. The data has successfully been used to organize international competitions within the Endoscopic Vision Challenges 2017 and 2019.},
	number = {{arXiv}:2005.03501},
	publisher = {{arXiv}},
	author = {Maier-Hein, Lena and Wagner, Martin and Ross, Tobias and Reinke, Annika and Bodenstedt, Sebastian and Full, Peter M. and Hempe, Hellena and Mindroc-Filimon, Diana and Scholz, Patrick and Tran, Thuy Nuong and Bruno, Pierangela and Kisilenko, Anna and M{\"{u}}ller, Benjamin and Davitashvili, Tornike and Capek, Manuela and Tizabi, Minu and Eisenmann, Matthias and Adler, Tim J. and Gr{\"{o}}hl, Janek and Schellenberg, Melanie and Seidlitz, Silvia and Lai, T. Y. Emmy and Pekdemir, B{\"{u}}nyamin and Roethlingshoefer, Veith and Both, Fabian and Bittel, Sebastian and Mengler, Marc and M{\"{u}}ndermann, Lars and Apitz, Martin and Kopp-Schneider, Annette and Speidel, Stefanie and Kenngott, Hannes G. and M{\"{u}}ller-Stich, Beat P.},
	urldate = {2025-09-16},
	date = {2021-02-23},
	eprinttype = {arxiv},
	eprint = {2005.03501 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {Preprint PDF:/home/kirchnema/Zotero/storage/W6AE8Y9I/Maier-Hein et al. - 2021 - Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room.pdf:application/pdf;Snapshot:/home/kirchnema/Zotero/storage/39WNL9MV/2005.html:text/html},
}


@article{ros_comparative_2021,
	title = {Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the {ROBUST}-{MIS} 2019 challenge},
	volume = {70},
	issn = {1361-8415},
	url = {https://www.sciencedirect.com/science/article/pii/S136184152030284X},
	doi = {10.1016/j.media.2020.101920},
	shorttitle = {Comparative validation of multi-instance instrument segmentation in endoscopy},
	abstract = {Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation ({ROBUST}-{MIS}) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).},
	pages = {101920},
	journaltitle = {Medical Image Analysis},
	shortjournal = {Medical Image Analysis},
	author = {Ro{\ss}, Tobias and Reinke, Annika and Full, Peter M. and Wagner, Martin and Kenngott, Hannes and Apitz, Martin and Hempe, Hellena and Mindroc-Filimon, Diana and Scholz, Patrick and Tran, Thuy Nuong and Bruno, Pierangela and Arbel{\'a}ez, Pablo and Bian, Gui-Bin and Bodenstedt, Sebastian and Bolmgren, Jon Lindstr{\"{o}}m and Bravo-S{\'a}nchez, Laura and Chen, Hua-Bin and Gonz{\'a}lez, Cristina and Guo, Dong and Halvorsen, P{\aa}l and Heng, Pheng-Ann and Hosgor, Enes and Hou, Zeng-Guang and Isensee, Fabian and Jha, Debesh and Jiang, Tingting and Jin, Yueming and Kirtac, Kadir and Kletz, Sabrina and Leger, Stefan and Li, Zhixuan and Maier-Hein, Klaus H. and Ni, Zhen-Liang and Riegler, Michael A. and Schoeffmann, Klaus and Shi, Ruohua and Speidel, Stefanie and Stenzel, Michael and Twick, Isabell and Wang, Gutai and Wang, Jiacheng and Wang, Liansheng and Wang, Lu and Zhang, Yujie and Zhou, Yan-Jie and Zhu, Lei and Wiesenfarth, Manuel and Kopp-Schneider, Annette and M{\"{u}}ller-Stich, Beat P. and Maier-Hein, Lena},
	urldate = {2025-09-16},
	date = {2021-05-01},
	keywords = {Minimally invasive surgery, Multi-instance instrument, Robustness and generalization, Surgical data science},
	file = {ScienceDirect Full Text PDF:/home/kirchnema/Zotero/storage/8BBJ4UF4/Ro{\ss} et al. - 2021 - Comparative validation of multi-instance instrument segmentation in endoscopy Results of the ROBUST.pdf:application/pdf;ScienceDirect Snapshot:/home/kirchnema/Zotero/storage/5L9KXERJ/S136184152030284X.html:text/html},
}


@incollection{valderrama_towards_2022,
	title = {Towards Holistic Surgical Scene Understanding},
	volume = {13437},
	url = {http://arxiv.org/abs/2212.04582},
	abstract = {Most benchmarks for studying surgical interventions focus on a specific challenge instead of leveraging the intrinsic complementarity among different tasks. In this work, we present a new experimental framework towards holistic surgical scene understanding. First, we introduce the Phase, Step, Instrument, and Atomic Visual Action recognition ({PSI}-{AVA}) Dataset. {PSI}-{AVA} includes annotations for both long-term (Phase and Step recognition) and short-term reasoning (Instrument detection and novel Atomic Action recognition) in robot-assisted radical prostatectomy videos. Second, we present Transformers for Action, Phase, Instrument, and steps Recognition ({TAPIR}) as a strong baseline for surgical scene understanding. {TAPIR} leverages our dataset's multi-level annotations as it benefits from the learned representation on the instrument detection task to improve its classification capacity. Our experimental results in both {PSI}-{AVA} and other publicly available databases demonstrate the adequacy of our framework to spur future research on holistic surgical scene understanding.},
	pages = {442--452},
	author = {Valderrama, Natalia and Puentes, Paola Ruiz and Hern{\'a}ndez, Isabela and Ayobi, Nicol{\'a}s and Verlyk, Mathilde and Santander, Jessica and Caicedo, Juan and Fern{\'a}ndez, Nicol{\'a}s and Arbel{\'a}ez, Pablo},
	urldate = {2025-09-16},
	date = {2022},
	doi = {10.1007/978-3-031-16449-1_42},
	eprinttype = {arxiv},
	eprint = {2212.04582 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition},
	file = {Snapshot:/home/kirchnema/Zotero/storage/PFJYSB7M/2212.html:text/html},
}


@misc{bawa_esad_2020,
	title = {{ESAD}: Endoscopic Surgeon Action Detection Dataset},
	url = {http://arxiv.org/abs/2006.07164},
	doi = {10.48550/arXiv.2006.07164},
	shorttitle = {{ESAD}},
	abstract = {In this work, we take aim towards increasing the effectiveness of surgical assistant robots. We intended to make assistant robots safer by making them aware about the actions of surgeon, so it can take appropriate assisting actions. In other words, we aim to solve the problem of surgeon action detection in endoscopic videos. To this, we introduce a challenging dataset for surgeon action detection in real-world endoscopic videos. Action classes are picked based on the feedback of surgeons and annotated by medical professional. Given a video frame, we draw bounding box around surgical tool which is performing action and label it with action label. Finally, we presenta frame-level action detection baseline model based on recent advances in ob-ject detection. Results on our new dataset show that our presented dataset provides enough interesting challenges for future method and it can serveas strong benchmark corresponding research in surgeon action detection in endoscopic videos.},
	number = {{arXiv}:2006.07164},
	publisher = {{arXiv}},
	author = {Bawa, Vivek Singh and Singh, Gurkirt and {KapingA}, Francis and Skarga-Bandurova, Inna and Leporini, Alice and Landolfo, Carmela and Stabile, Armando and Setti, Francesco and Muradore, Riccardo and Oleari, Elettra and Cuzzolin, Fabio},
	urldate = {2025-09-16},
	date = {2020-06-12},
	eprinttype = {arxiv},
	eprint = {2006.07164 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics},
	file = {Full Text PDF:/home/kirchnema/Zotero/storage/E8MVJ6UZ/Bawa et al. - 2020 - ESAD Endoscopic Surgeon Action Detection Dataset.pdf:application/pdf;Snapshot:/home/kirchnema/Zotero/storage/D32GQ9CM/2006.html:text/html},
}


@inproceedings{leibetseder_lapgyn4_2018,
	location = {New York, {NY}, {USA}},
	title = {Lapgyn4: a dataset for 4 automatic content analysis problems in the domain of laparoscopic gynecology},
	isbn = {978-1-4503-5192-8},
	url = {https://dl.acm.org/doi/10.1145/3204949.3208127},
	doi = {10.1145/3204949.3208127},
	series = {{MMSys} '18},
	shorttitle = {Lapgyn4},
	abstract = {Modern imaging technology enables medical practitioners to perform minimally invasive surgery ({MIS}), i.e. a variety of medical interventions inflicting minimal trauma upon patients, hence, greatly improving their recoveries. Not only patients but also surgeons can benefit from this technology, as recorded media can be utilized for speeding-up tedious and time-consuming tasks such as treatment planning or case documentation. In order to improve the predominantly manually conducted process of analyzing said media, with this work we publish four datasets extracted from gynecologic, laparoscopic interventions with the intend on encouraging research in the field of post-surgical automatic media analysis. These datasets are designed with the following use cases in mind: medical image retrieval based on a query image, detection of instrument counts, surgical actions and anatomical structures, as well as distinguishing on which anatomical structure a certain action is performed. Furthermore, we provide suggestions for evaluation metrics and first baseline experiments.},
	pages = {357--362},
	booktitle = {Proceedings of the 9th {ACM} Multimedia Systems Conference},
	publisher = {Association for Computing Machinery},
	author = {Leibetseder, Andreas and Petscharnig, Stefan and Primus, Manfred J{\"{u}}rgen and Kletz, Sabrina and M{\"{u}}nzer, Bernd and Schoeffmann, Klaus and Keckstein, J{\"{o}}rg},
	urldate = {2025-09-16},
	date = {2018-06-12},
	file = {Full Text PDF:/home/kirchnema/Zotero/storage/NXVKYXE3/Leibetseder et al. - 2018 - Lapgyn4 a dataset for 4 automatic content analysis problems in the domain of laparoscopic gynecolog.pdf:application/pdf},
}


@inproceedings{yoon_hsdb-instrument_2021,
	location = {Cham},
	title = {{hSDB}-instrument: Instrument Localization Database for Laparoscopic and Robotic Surgeries},
	isbn = {978-3-030-87202-1},
	doi = {10.1007/978-3-030-87202-1_38},
	shorttitle = {{hSDB}-instrument},
	abstract = {Automated surgical instrument localization is an important technology to understand the surgical process and in order to analyze them to provide meaningful guidance during surgery or surgical index after surgery to the surgeon. We introduce a new dataset that reflects the kinematic characteristics of surgical instruments for automated surgical instrument localization of surgical videos. The {hSDB}(hutom Surgery {DataBase})-instrument dataset consists of instrument localization information from 24 cases of laparoscopic cholecystecomy and 24 cases of robotic gastrectomy. Localization information for all instruments is provided in the form of a bounding box for object detection. To handle class imbalance problem between instruments, synthesized instruments modeled in Unity for 3D models are included as training data. Besides, for 3D instrument data, a polygon annotation is provided to enable instance segmentation of the tool. To reflect the kinematic characteristics of all instruments, they are annotated with head and body parts for laparoscopic instruments, and with head, wrist, and body parts for robotic instruments separately. Annotation data of assistive tools (specimen bag, needle, etc.) that are frequently used for surgery are also included. Moreover, we provide statistical information on the {hSDB}-instrument dataset and the baseline localization performances of the object detection networks trained by the {MMDetection} library and resulting analyses (The dataset, additional dataset statistics and several trained models are publicly available at https://hsdb-instrument.github.io/).},
	pages = {393--402},
	booktitle = {Medical Image Computing and Computer Assisted Intervention -- {MICCAI} 2021},
	publisher = {Springer International Publishing},
	author = {Yoon, Jihun and Lee, Jiwon and Heo, Sunghwan and Yu, Hayeong and Lim, Jayeon and Song, Chi Hyun and Hong, {SeulGi} and Hong, Seungbum and Park, Bokyung and Park, {SungHyun} and Hyung, Woo Jin and Choi, Min-Kook},
	editor = {de Bruijne, Marleen and Cattin, Philippe C. and Cotin, St{\'e}phane and Padoy, Nicolas and Speidel, Stefanie and Zheng, Yefeng and Essert, Caroline},
	date = {2021},
	langid = {english},
	keywords = {Class imbalance, Domain randomization, Object detection, Surgical instrument localization},
	file = {Full Text PDF:/home/kirchnema/Zotero/storage/X7FF7Y6B/Yoon et al. - 2021 - hSDB-instrument Instrument Localization Database for Laparoscopic and Robotic Surgeries.pdf:application/pdf},
}


@article{carstens_dresden_2023,
	title = {The Dresden Surgical Anatomy Dataset for Abdominal Organ Segmentation in Surgical Data Science},
	volume = {10},
	rights = {2023 The Author(s)},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-022-01719-2},
	doi = {10.1038/s41597-022-01719-2},
	abstract = {Laparoscopy is an imaging technique that enables minimally-invasive procedures in various medical disciplines including abdominal surgery, gynaecology and urology. To date, publicly available laparoscopic image datasets are mostly limited to general classifications of data, semantic segmentations of surgical instruments and low-volume weak annotations of specific abdominal organs. The Dresden Surgical Anatomy Dataset provides semantic segmentations of eight abdominal organs (colon, liver, pancreas, small intestine, spleen, stomach, ureter, vesicular glands), the abdominal wall and two vessel structures (inferior mesenteric artery, intestinal veins) in laparoscopic view. In total, this dataset comprises 13195 laparoscopic images. For each anatomical structure, we provide over a thousand images with pixel-wise segmentations. Annotations comprise semantic segmentations of single organs and one multi-organ-segmentation dataset including segments for all eleven anatomical structures. Moreover, we provide weak annotations of organ presence for every single image. This dataset markedly expands the horizon for surgical data science applications of computer vision in laparoscopic surgery and could thereby contribute to a reduction of risks and faster translation of Artificial Intelligence into surgical practice.},
	pages = {3},
	number = {1},
	journaltitle = {Scientific Data},
	shortjournal = {Sci Data},
	author = {Carstens, Matthias and Rinner, Franziska M. and Bodenstedt, Sebastian and Jenke, Alexander C. and Weitz, J{\"{u}}rgen and Distler, Marius and Speidel, Stefanie and Kolbinger, Fiona R.},
	urldate = {2025-09-16},
	date = {2023-01-12},
	langid = {english},
	note = {Publisher: Nature Publishing Group},
	keywords = {Endoscopy, Gastrointestinal system, Translational research},
	file = {Full Text PDF:/home/kirchnema/Zotero/storage/FISCFS5W/Carstens et al. - 2023 - The Dresden Surgical Anatomy Dataset for Abdominal Organ Segmentation in Surgical Data Science.pdf:application/pdf},
}


@inproceedings{leibetseder_glenda_2020,
	location = {Cham},
	title = {{GLENDA}: Gynecologic Laparoscopy Endometriosis Dataset},
	isbn = {978-3-030-37734-2},
	doi = {10.1007/978-3-030-37734-2_36},
	shorttitle = {{GLENDA}},
	abstract = {Gynecologic laparoscopy as a type of minimally invasive surgery ({MIS}) is performed via a live feed of a patient's abdomen surveying the insertion and handling of various instruments for conducting treatment. Adopting this kind of surgical intervention not only facilitates a great variety of treatments, the possibility of recording said video streams is as well essential for numerous post-surgical activities, such as treatment planning, case documentation and education. Nonetheless, the process of manually analyzing surgical recordings, as it is carried out in current practice, usually proves tediously time-consuming. In order to improve upon this situation, more sophisticated computer vision as well as machine learning approaches are actively developed. Since most of such approaches heavily rely on sample data, which especially in the medical field is only sparsely available, with this work we publish the Gynecologic Laparoscopy {ENdometriosis} {DAtaset} ({GLENDA}) -- an image dataset containing region-based annotations of a common medical condition named endometriosis, i.e. the dislocation of uterine-like tissue. The dataset is the first of its kind and it has been created in collaboration with leading medical experts in the field.},
	pages = {439--450},
	booktitle = {{MultiMedia} Modeling},
	publisher = {Springer International Publishing},
	author = {Leibetseder, Andreas and Kletz, Sabrina and Schoeffmann, Klaus and Keckstein, Simon and Keckstein, J{\"{o}}rg},
	editor = {Ro, Yong Man and Cheng, Wen-Huang and Kim, Junmo and Chu, Wei-Ta and Cui, Peng and Choi, Jung-Woo and Hu, Min-Chun and De Neve, Wesley},
	date = {2020},
	langid = {english},
	keywords = {Endometriosis localization, Gynecologic laparoscopy, Lesion detection, Medical dataset, Region-based annotations},
	file = {Full Text PDF:/home/kirchnema/Zotero/storage/WWI2UIDN/Leibetseder et al. - 2020 - GLENDA Gynecologic Laparoscopy Endometriosis Dataset.pdf:application/pdf},
}


@article{schoeffmann_video_2018,
	title = {Video retrieval in laparoscopic video recordings with dynamic content descriptors},
	volume = {77},
	issn = {1573-7721},
	url = {https://doi.org/10.1007/s11042-017-5252-2},
	doi = {10.1007/s11042-017-5252-2},
	abstract = {In the domain of gynecologic surgery an increasing number of surgeries are performed in a minimally invasive manner. These laparoscopic surgeries require specific psychomotor skills of the operating surgeon, which are difficult to learn and teach. This is the reason why an increasing number of surgeons promote checking video recordings of laparoscopic surgeries for the occurrence of technical errors with surgical actions. This manual surgical quality assessment ({SQA}) process, however, is very cumbersome and time-consuming when carried out without any support from content-based video retrieval. In this work we propose a video content descriptor called {MIDD} (Motion Intensity and Direction Descriptor) that can be effectively used to find similar segments in a laparoscopic video database and thereby help surgeons to more quickly inspect other instances of a given error scene. We evaluate the retrieval performance of {MIDD} with surgical actions from gynecologic surgery in direct comparison to several other dynamic content descriptors. We show that the {MIDD} descriptor significantly outperforms the state-of-the-art in terms of retrieval performance as well as in terms of runtime performance. Additionally, we release the manually created video dataset of 16 classes of surgical actions from medical laparoscopy to the public, for further evaluations.},
	pages = {16813--16832},
	number = {13},
	journaltitle = {Multimedia Tools and Applications},
	shortjournal = {Multimed Tools Appl},
	author = {Schoeffmann, Klaus and Husslein, Heinrich and Kletz, Sabrina and Petscharnig, Stefan and Muenzer, Bernd and Beecks, Christian},
	urldate = {2025-09-16},
	date = {2018-07-01},
	langid = {english},
	keywords = {Laparoscopic video, Medical multimedia, Similarity search, Surgical quality assessment, Video descriptor, Video retrieval},
	file = {Full Text PDF:/home/kirchnema/Zotero/storage/TTW489ZT/Schoeffmann et al. - 2018 - Video retrieval in laparoscopic video recordings with dynamic content descriptors.pdf:application/pdf},
}



@misc{liu_fedfms_2024,
	title = {{FedFMS}: Exploring Federated Foundation Models for Medical Image Segmentation},
	url = {http://arxiv.org/abs/2403.05408},
	doi = {10.48550/arXiv.2403.05408},
	shorttitle = {{FedFMS}},
	abstract = {Medical image segmentation is crucial for clinical diagnosis. The Segmentation Anything Model ({SAM}) serves as a powerful foundation model for visual segmentation and can be adapted for medical image segmentation. However, medical imaging data typically contain privacy-sensitive information, making it challenging to train foundation models with centralized storage and sharing. To date, there are few foundation models tailored for medical image deployment within the federated learning framework, and the segmentation performance, as well as the efficiency of communication and training, remain unexplored. In response to these issues, we developed Federated Foundation models for Medical image Segmentation ({FedFMS}), which includes the Federated {SAM} ({FedSAM}) and a communication and training-efficient Federated {SAM} with Medical {SAM} Adapter ({FedMSA}). Comprehensive experiments on diverse datasets are conducted to investigate the performance disparities between centralized training and federated learning across various configurations of {FedFMS}. The experiments revealed that {FedFMS} could achieve performance comparable to models trained via centralized training methods while maintaining privacy. Furthermore, {FedMSA} demonstrated the potential to enhance communication and training efficiency. Our model implementation codes are available at https://github.com/{LIU}-{YUXI}/{FedFMS}.},
	number = {{arXiv}:2403.05408},
	publisher = {{arXiv}},
	author = {Liu, Yuxi and Luo, Guibo and Zhu, Yuesheng},
	urldate = {2025-09-30},
	date = {2024-11-06},
	eprinttype = {arxiv},
	eprint = {2403.05408 [eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Distributed, Parallel, and Cluster Computing, Electrical Engineering and Systems Science - Image and Video Processing},
	file = {Preprint PDF:/home/kirchnema/Zotero/storage/GQTY9HNU/Liu et al. - 2024 - FedFMS Exploring Federated Foundation Models for Medical Image Segmentation.pdf:application/pdf;Snapshot:/home/kirchnema/Zotero/storage/II78VBCX/2403.html:text/html},
    year = 2024
}

@misc{do_fedefm_2025,
	title = {{FedEFM}: Federated Endovascular Foundation Model with Unseen Data},
	url = {http://arxiv.org/abs/2501.16992},
	doi = {10.48550/arXiv.2501.16992},
	shorttitle = {{FedEFM}},
	abstract = {In endovascular surgery, the precise identification of catheters and guidewires in X-ray images is essential for reducing intervention risks. However, accurately segmenting catheter and guidewire structures is challenging due to the limited availability of labeled data. Foundation models offer a promising solution by enabling the collection of similar domain data to train models whose weights can be fine-tuned for downstream tasks. Nonetheless, large-scale data collection for training is constrained by the necessity of maintaining patient privacy. This paper proposes a new method to train a foundation model in a decentralized federated learning setting for endovascular intervention. To ensure the feasibility of the training, we tackle the unseen data issue using differentiable Earth Mover's Distance within a knowledge distillation framework. Once trained, our foundation model's weights provide valuable initialization for downstream tasks, thereby enhancing task-specific performance. Intensive experiments show that our approach achieves new state-of-the-art results, contributing to advancements in endovascular intervention and robotic-assisted endovascular surgery, while addressing the critical issue of data sharing in the medical domain.},
	number = {{arXiv}:2501.16992},
	publisher = {{arXiv}},
	author = {Do, Tuong and Vu, Nghia and Jianu, Tudor and Huang, Baoru and Vu, Minh and Su, Jionglong and Tjiputra, Erman and Tran, Quang D. and Chiu, Te-Chuan and Nguyen, Anh},
	urldate = {2025-09-30},
	date = {2025-01-28},
	eprinttype = {arxiv},
	eprint = {2501.16992 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {Snapshot:/home/kirchnema/Zotero/storage/V3S3MZXR/2501.html:text/html},
    year = 2025
}

@misc{kolbinger_appendix300_2025,
	title = {Appendix300: A multi-institutional laparoscopic appendectomy video dataset for computational modeling tasks},
	rights = {{\copyright} 2025, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-{NonCommercial}-{NoDerivs} 4.0 International), {CC} {BY}-{NC}-{ND} 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
	url = {https://www.medrxiv.org/content/10.1101/2025.09.05.25335174v1},
	doi = {10.1101/2025.09.05.25335174},
	shorttitle = {Appendix300},
	abstract = {The limited availability of diverse and representative training data poses a critical barrier to the development of clinically relevant computational tools for intraoperative surgical decision support. As surgical interventions are not routinely recorded and data annotation often requires domain knowledge, existing open-access surgical video datasets with high-quality annotations are available only for a restricted number of surgical procedures, such as cholecystectomy, and typically originate from a single institution. Appendix300 is a comprehensive dataset of video footage from 330 surgical procedures, including 312 full-length recordings of laparoscopic appendectomies in pediatric and adult patients treated at five German centers, along with 18 control recordings of non-inflamed appendices collected during non-appendectomy laparoscopic surgeries. Clinical metadata for each recording includes patient demographics, medical history, clinical symptoms, preoperative laboratory parameters, and postoperative histopathological findings. Additionally, we provide annotations of the grade of appendicitis based on the intraoperative presentation. This dataset enables new and clinically relevant validation tasks for computer vision in laparoscopic surgery, thereby enhancing the breadth and translational relevance of {AI}-based surgical video analysis.},
	publisher = {{medRxiv}},
	author = {Kolbinger, Fiona R. and Kirchner, Max and Pfeiffer, Kevin and Bodenstedt, Sebastian and Jenke, Alexander C. and Barthel, Julia and Carstens, Matthias R. and Dehlke, Karolin and Dietz, Sophia and Emmanouilidis, Sotirios and Fitze, Guido and Leitermann, Linda and Mees, S{\"{o}}ren Torge and Pistorius, Steffen and Prudlo, Conrad and Seiberth, Astrid and Schultz, Jurek and Thiel, Karolin and Ziehn, Daniel and Speidel, Stefanie and Weitz, J{\"{u}}rgen and Kather, Jakob Nikolas and Distler, Marius and Saldanha, Oliver Lester},
	urldate = {2025-09-08},
	date = {2025-09-07},
	langid = {english},
	note = {{ISSN}: 3067-2007
Pages: 2025.09.05.25335174},
    year = 2025
}


@misc{nasirihaghighi_gynsurg_2025,
	title = {{GynSurg}: A Comprehensive Gynecology Laparoscopic Surgery Dataset},
	url = {http://arxiv.org/abs/2506.11356},
	doi = {10.48550/arXiv.2506.11356},
	shorttitle = {{GynSurg}},
	abstract = {Recent advances in deep learning have transformed computer-assisted intervention and surgical video analysis, driving improvements not only in surgical training, intraoperative decision support, and patient outcomes, but also in postoperative documentation and surgical discovery. Central to these developments is the availability of large, high-quality annotated datasets. In gynecologic laparoscopy, surgical scene understanding and action recognition are fundamental for building intelligent systems that assist surgeons during operations and provide deeper analysis after surgery. However, existing datasets are often limited by small scale, narrow task focus, or insufficiently detailed annotations, limiting their utility for comprehensive, end-to-end workflow analysis. To address these limitations, we introduce {GynSurg}, the largest and most diverse multi-task dataset for gynecologic laparoscopic surgery to date. {GynSurg} provides rich annotations across multiple tasks, supporting applications in action recognition, semantic segmentation, surgical documentation, and discovery of novel procedural insights. We demonstrate the dataset quality and versatility by benchmarking state-of-the-art models under a standardized training protocol. To accelerate progress in the field, we publicly release the {GynSurg} dataset and its annotations},
	number = {{arXiv}:2506.11356},
	publisher = {{arXiv}},
	author = {Nasirihaghighi, Sahar and Ghamsarian, Negin and Peschek, Leonie and Munari, Matteo and Husslein, Heinrich and Sznitman, Raphael and Schoeffmann, Klaus},
	urldate = {2025-10-09},
	date = {2025-06-12},
	eprinttype = {arxiv},
	eprint = {2506.11356 [cs]},
	note = {version: 1},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {Preprint PDF:/home/kirchnema/Zotero/storage/BRK2AU9S/Nasirihaghighi et al. - 2025 - GynSurg A Comprehensive Gynecology Laparoscopic Surgery Dataset.pdf:application/pdf;Snapshot:/home/kirchnema/Zotero/storage/8FQ3VPR2/2506.html:text/html},
    year = 2025
}


@online{rights_ocr_health_2021,
	title = {Health Information Privacy},
	url = {https://www.hhs.gov/hipaa/index.html},
	abstract = {Health Information Privacy},
	type = {Landing Page},
	author = {Rights ({OCR}), Office for Civil},
	urldate = {2025-07-31},
	date = {2021-06-09},
	langid = {english},
	note = {Last Modified: 2025-06-27T11:38:47-0400},
	file = {Snapshot:/home/kirchnema/Zotero/storage/PFWL48V9/index.html:text/html},
    year = 2021,
}


@online{noauthor_general_nodate,
	title = {General Data Protection Regulation ({GDPR}) -- Legal Text},
	url = {https://gdpr-info.eu/},
	abstract = {The official {PDF} of the Regulation ({EU}) 2016/679 -- known as {GDPR} -- its recitals \& key issues as a neatly arranged website.},
	titleaddon = {General Data Protection Regulation ({GDPR})},
	urldate = {2025-07-31},
	langid = {american},
	file = {Snapshot:/home/kirchnema/Zotero/storage/Y69XLWIQ/gdpr-info.eu.html:text/html},
    year = 2016,
}

@misc{kirillov_segment_2023,
	title = {Segment Anything},
	url = {http://arxiv.org/abs/2304.02643},
	doi = {10.48550/arXiv.2304.02643},
	abstract = {We introduce the Segment Anything ({SA}) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model ({SAM}) and corresponding dataset ({SA}-1B) of 1B masks and 11M images at https://segment-anything.com to foster research into foundation models for computer vision.},
	number = {{arXiv}:2304.02643},
	publisher = {{arXiv}},
	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 Doll{\'a}r, Piotr and Girshick, Ross},
	urldate = {2025-10-16},
	date = {2023-04-05},
	eprinttype = {arxiv},
	eprint = {2304.02643 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	file = {Snapshot:/home/kirchnema/Zotero/storage/ZJM8J2WZ/2304.html:text/html},
    year = 2023
}


@misc{he_momentum_2020,
	title = {Momentum Contrast for Unsupervised Visual Representation Learning},
	url = {http://arxiv.org/abs/1911.05722},
	doi = {10.48550/arXiv.1911.05722},
	abstract = {We present Momentum Contrast ({MoCo}) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. {MoCo} provides competitive results under the common linear protocol on {ImageNet} classification. More importantly, the representations learned by {MoCo} transfer well to downstream tasks. {MoCo} can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on {PASCAL} {VOC}, {COCO}, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks.},
	number = {{arXiv}:1911.05722},
	publisher = {{arXiv}},
	author = {He, Kaiming and Fan, Haoqi and Wu, Yuxin and Xie, Saining and Girshick, Ross},
	urldate = {2025-10-27},
	date = {2020-03-23},
	eprinttype = {arxiv},
	eprint = {1911.05722 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {Preprint PDF:/home/kirchnema/Zotero/storage/MJISWUHR/He et al. - 2020 - Momentum Contrast for Unsupervised Visual Representation Learning.pdf:application/pdf;Snapshot:/home/kirchnema/Zotero/storage/JQIWUYRY/1911.html:text/html},
    year = 2020
}
