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

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

doi:10.1109/tmi.2022.3222126

Cited by 30 publications

(10 citation statements)

References 32 publications

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“…Three studies focused on skin images, tackling issues such as skin disease detection using the Dermatology Atlas dataset [53,54] and melanoma detection with the dermoscopic skin lesion image dataset [56]. Two studies involved ultrasound images [34,43], and the final two studies used other image types, specifically fundus [48] and surgical images [55]. The use of free text in studies was primarily associated with natural language processing, as evidenced by six studies.…”

Section: Data Typesmentioning

confidence: 99%

Survey of Medical Applications of Federated Learning

Choi,

Cha,

Lee

et al. 2024

Healthc Inform Res

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Objectives: Medical artificial intelligence (AI) has recently attracted considerable attention. However, training medical AI models is challenging due to privacy-protection regulations. Among the proposed solutions, federated learning (FL) stands out. FL involves transmitting only model parameters without sharing the original data, making it particularly suitable for the medical field, where data privacy is paramount. This study reviews the application of FL in the medical domain.Methods: We conducted a literature search using the keywords "federated learning" in combination with "medical," "healthcare," or "clinical" on Google Scholar and PubMed. After reviewing titles and abstracts, 58 papers were selected for analysis. These FL studies were categorized based on the types of data used, the target disease, the use of open datasets, the local model of FL, and the neural network model. We also examined issues related to heterogeneity and security.Results: In the investigated FL studies, the most commonly used data type was image data, and the most studied target diseases were cancer and COVID-19. The majority of studies utilized open datasets. Furthermore, 72% of the FL articles addressed heterogeneity issues, while 50% discussed security concerns.Conclusions: FL in the medical domain appears to be in its early stages, with most research using open data and focusing on specific data types and diseases for performance verification purposes. Nonetheless, medical FL research is anticipated to be increasingly applied and to become a vital component of multi-institutional research.

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Section: Data Typesmentioning

confidence: 99%

Survey of Medical Applications of Federated Learning

Choi,

Cha,

Lee

et al. 2024

Healthc Inform Res

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“…Although great progress has been achieved by existing methods for multi-organ segmentation and other tasks in medical image analysis, these methods are primarily for a single task, i.e., SSL [7,8], PSL [9,10], and FL [13,14], and dual tasks, e.g., federated semi-supervised learning [17,18] and federated partial-label learning [15,16]. Unlike previous works, this work introduces a more challenging and practical setting in multi-organ segmentation, which aims to learn a federated model from distributed, partially labeled, and unlabeled datasets by unifying SSL, PSL, and FL.…”

Section: Related Workmentioning

confidence: 99%

Federated 3D multi-organ segmentation with partially labeled and unlabeled data

Zheng,

Hayashi,

Oda

et al. 2024

Int J CARS

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Purpose This paper considers a new problem setting for multi-organ segmentation based on the following observations. In reality, (1) collecting a large-scale dataset from various institutes is usually impeded due to privacy issues; (2) many images are not labeled since the slice-by-slice annotation is costly; and (3) datasets may exhibit inconsistent, partial annotations across different institutes. Learning a federated model from these distributed, partially labeled, and unlabeled samples is an unexplored problem. Methods To simulate this multi-organ segmentation problem, several distributed clients and a central server are maintained. The central server coordinates with clients to learn a global model using distributed private datasets, which comprise a small part of partially labeled images and a large part of unlabeled images. To address this problem, a practical framework that unifies partially supervised learning (PSL), semi-supervised learning (SSL), and federated learning (FL) paradigms with PSL, SSL, and FL modules is proposed. The PSL module manages to learn from partially labeled samples. The SSL module extracts valuable information from unlabeled data. Besides, the FL module aggregates local information from distributed clients to generate a global statistical model. With the collaboration of three modules, the presented scheme could take advantage of these distributed imperfect datasets to train a generalizable model. Results The proposed method was extensively evaluated with multiple abdominal CT datasets, achieving an average result of 84.83% in Dice and 41.62 mm in 95HD for multi-organ (liver, spleen, and stomach) segmentation. Moreover, its efficacy in transfer learning further demonstrated its good generalization ability for downstream segmentation tasks. Conclusion This study considers a novel problem of multi-organ segmentation, which aims to develop a generalizable model using distributed, partially labeled, and unlabeled CT images. A practical framework is presented, which, through extensive validation, has proved to be an effective solution, demonstrating strong potential in addressing this challenging problem.

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“…A noteworthy variation was conducted by Kassem et al, who used surgical videos rather than images to apply FL for surgical phase detection (not diagnosis) on Cholecystecotomy procedures [104]. RQ1: How does federated learning differ from centralized learning when dealing with medical imaging applications?…”

Section: Othersmentioning

confidence: 99%

Medical Imaging Applications of Federated Learning

Sandhu,

Gorji,

Tavakolian

et al. 2023

Diagnostics

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Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL) to several domains ranging from edge computing to banking. The technique’s inherent security benefits, privacy-preserving capabilities, ease of scalability, and ability to transcend data biases have motivated researchers to use this tool on healthcare datasets. While several reviews exist detailing FL and its applications, this review focuses solely on the different applications of FL to medical imaging datasets, grouping applications by diseases, modality, and/or part of the body. This Systematic Literature review was conducted by querying and consolidating results from ArXiv, IEEE Xplorer, and PubMed. Furthermore, we provide a detailed description of FL architecture, models, descriptions of the performance achieved by FL models, and how results compare with traditional Machine Learning (ML) models. Additionally, we discuss the security benefits, highlighting two primary forms of privacy-preserving techniques, including homomorphic encryption and differential privacy. Finally, we provide some background information and context regarding where the contributions lie. The background information is organized into the following categories: architecture/setup type, data-related topics, security, and learning types. While progress has been made within the field of FL and medical imaging, much room for improvement and understanding remains, with an emphasis on security and data issues remaining the primary concerns for researchers. Therefore, improvements are constantly pushing the field forward. Finally, we highlighted the challenges in deploying FL in medical imaging applications and provided recommendations for future directions.

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

Cited by 30 publications

References 32 publications

Survey of Medical Applications of Federated Learning

Survey of Medical Applications of Federated Learning

Federated 3D multi-organ segmentation with partially labeled and unlabeled data

Medical Imaging Applications of Federated Learning

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