Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases

Kassem, Hasan; Alapatt, Deepak; Mascagni, Pietro; Consortium, AI4SafeChole; Karargyris, Alexandros; Padoy, Nicolas

doi:10.48550/arxiv.2203.07345

Cited by 4 publications

(4 citation statements)

References 37 publications

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“…Future work could look beyond the considered datasets and tasks. The generalizability of these features across surgical centers or their integration with decentralized learning techniques such as federated learning (McMahan et al, 2017) may enable research into more robust model designs for surgical applications (Kassem et al, 2022) and eventually real-world deployment. Further, studies on the generalizability of representations learned across surgical procedures may significantly boost the utility of the few, precious datasets that are publicly available for surgical data science research.…”

Section: Discussionmentioning

confidence: 99%

Dissecting Self-Supervised Learning Methods for Surgical Computer Vision

Ramesh¹,

Srivastav²,

Alapatt³

et al. 2022

Preprint

Self Cite

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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% 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%. The code will be made available at https://github.com/CAMMA-public/SelfSupSurg.

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Section: Discussionmentioning

confidence: 99%

Dissecting Self-Supervised Learning Methods for Surgical Computer Vision

Ramesh¹,

Srivastav²,

Alapatt³

et al. 2022

Preprint

Self Cite

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“…However, unlabelled datasets could grow in numbers when small health organizations and big hospitals participate in the FL for training a global model. To overcome this unlabelled datasets problem, semi-supervised and unsupervised learning have recently been used in FL by some researchers [208]- [211]. Although semi-supervised and unsupervised learning can solve the problem related to the unlabelled datasets, they often fail to achieve high performance in medical image datasets with DL models.…”

Section: Contrastive Learning For Unlabelled Medical Datamentioning

confidence: 99%

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

Rauniyar,

Hagos,

Jha

et al. 2024

IEEE Internet Things J.

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With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable FL models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifying open problems and future research directions.

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“…Phases are elements of surgical workflows necessary to successfully complete procedures and are usually defined by consensus and annotated on surgical videos [ 40 ]. Accurate AI-based surgical phase recognition has today been demonstrated in laparoscopic cholecystectomy [ 41 , 42 ], gastric bypass surgery [ 17 ], sleeve gastrectomy [ 18 ], inguinal hernia repair [ 16 ], and peroral endoscopic myotomy (POEM) procedures [ 43 ].…”

Section: State-of-the-art Of the Intraoperative Application Of Aimentioning

confidence: 99%

Artificial Intelligence in Colorectal Cancer Surgery: Present and Future Perspectives

Quero

Mascagni

Kolbinger

et al. 2022

Cancers

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Artificial intelligence (AI) and computer vision (CV) are beginning to impact medicine. While evidence on the clinical value of AI-based solutions for the screening and staging of colorectal cancer (CRC) is mounting, CV and AI applications to enhance the surgical treatment of CRC are still in their early stage. This manuscript introduces key AI concepts to a surgical audience, illustrates fundamental steps to develop CV for surgical applications, and provides a comprehensive overview on the state-of-the-art of AI applications for the treatment of CRC. Notably, studies show that AI can be trained to automatically recognize surgical phases and actions with high accuracy even in complex colorectal procedures such as transanal total mesorectal excision (TaTME). In addition, AI models were trained to interpret fluorescent signals and recognize correct dissection planes during total mesorectal excision (TME), suggesting CV as a potentially valuable tool for intraoperative decision-making and guidance. Finally, AI could have a role in surgical training, providing automatic surgical skills assessment in the operating room. While promising, these proofs of concept require further development, validation in multi-institutional data, and clinical studies to confirm AI as a valuable tool to enhance CRC treatment.

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Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases

Cited by 4 publications

References 37 publications

Dissecting Self-Supervised Learning Methods for Surgical Computer Vision

Dissecting Self-Supervised Learning Methods for Surgical Computer Vision

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

Artificial Intelligence in Colorectal Cancer Surgery: Present and Future Perspectives

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