A Systematic Review on Federated Learning in Medical Image Analysis

Sohan, Fahimuzzman; Basalamah, Anas

doi:10.1109/access.2023.3260027

Cited by 20 publications

(3 citation statements)

References 86 publications

(169 reference statements)

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“…For instance, explainable AI [84] is an increasingly important topic that enables clinicians to better interpret and explain the predictions made by learning models. Similarly, Federated Learning [85] is another recent advancement in the field of deep learning that allows multiple institutions to collaboratively train models on their respective datasets without sharing sensitive patient data. By being aware of these emerging concepts and technologies, newcomers can broaden their horizons and enhance their understanding of the latest trends and developments in the field of medical diagnostic using deep learning.…”

Section: Discussionmentioning

confidence: 99%

A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques

et al. 2023

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The automated analysis of medical diagnostic videos, such as ultrasound and endoscopy, provides significant benefits in clinical practice by improving the efficiency and accuracy of diagnosis. Deep learning techniques show remarkable success in analyzing these videos by automating tasks such as classification, detection, and segmentation. In this paper, we review the application of deep learning techniques for analyzing medical diagnostic videos, with a focus on ultrasound and endoscopy. The methodology for selecting the papers consists of two major steps. First, we selected around 350 papers based on the relevance of their titles to our topic. Second, we chose the research articles that focus on deep learning and medical diagnostic videos based on our inclusion and exclusion criteria. We found that convolutional neural networks (CNNs) and long short-term memory (LSTM) are the two most commonly used models that achieve good results in analyzing different types of medical videos. We also found various limitations and open challenges. We highlight the limitations and open challenges in this field, such as labeling and preprocessing of medical videos, class imbalance, and time complexity, as well as incorporating expert knowledge, k-shot learning, live feedback from experts, and medical history with video data. Our review can encourage collaborative research with domain experts and patients to improve the diagnosis of diseases from medical videos.

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Section: Discussionmentioning

confidence: 99%

A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques

et al. 2023

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“…Traditional methods for tumor detection include region‐growing, thresholding, and level set. Machine learning‐based detection methods involve algorithms such as support vector machines (SVMs), AdaBoost classifier [28], and other similar techniques. These methods utilize machine learning algorithms to learn patterns and classify tumors in medical images.…”

Section: Related Workmentioning

confidence: 99%

A Federated Learning Framework for Brain Tumor Segmentation Without Sharing Patient Data

Zhang,

Jin,

Rho

et al. 2024

Int J Imaging Syst Tech

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Brain tumors pose a significant threat to human health, necessitating early detection and accurate diagnosis to enhance treatment outcomes. However, centralized data collection and processing encounter challenges related to privacy breaches and data integration due to the sensitivity and diversity of brain tumor patient data. In response, this paper proposes an innovative federated learning‐based approach for brain tumor detection, facilitating multicenter data sharing while safeguarding individual data privacy. Our proposed federated learning architecture features each medical center as a participant, with each retaining local data and engaging in secure communication with a central server. Within this federated migration learning framework, each medical center independently trains a base model on its local data and transmits a fraction of the model's parameters to the central server. The central server leverages these parameters for model aggregation and knowledge sharing, facilitating the exchange and migration of models among participating medical centers. This collaborative approach empowers individual medical centers to share knowledge and experiences, thereby enhancing the performance and accuracy of the brain tumor detection model. To validate our federated learning model, we conduct comprehensive evaluations using an independent test dataset, comparing its performance with traditional centralized learning approaches. The experimental results underscore the superiority of the federated learning‐based brain tumor detection approach, achieving heightened detection performance compared with traditional methods while meticulously preserving data privacy. In conclusion, our study presents an innovative solution for effective data collaboration and privacy protection in the realm of brain tumor detection, holding promising clinical applications. The federated learning approach not only advances detection accuracy but also establishes a secure and privacy‐preserving foundation for collaborative research in medical imaging.

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“…This approach allows for a more comprehensive and diverse set of data from which the model can learn, leading to greater accuracy in predicting and detecting diseases in their early stages. In preliminary health diagnosis, the implementation of federated learning is an effective mechanism to develop predictive models that can predict the likelihood of diseases such as cancer, diabetes, and heart disease [72][73][74]. By thoroughly analyzing data collected from different patients, the model can efficiently identify patterns and risk factors that would be difficult to detect in a single patient.…”

Section: Federated Learning (Fl)mentioning

confidence: 99%

Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions

Badidi

2023

Future Internet

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Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. Edge AI encompasses data analytics and artificial intelligence (AI) using machine learning, deep learning, and federated learning models deployed and executed at the edge of the network, far from centralized data centers. AI enables the careful analysis of large datasets derived from multiple sources, including electronic health records, wearable devices, and demographic information, making it possible to identify intricate patterns and predict a person’s future health. Federated learning, a novel approach in AI, further enhances this prediction by enabling collaborative training of AI models on distributed edge devices while maintaining privacy. Using edge computing, data can be processed and analyzed locally, reducing latency and enabling instant decision making. This article reviews the role of Edge AI in early health prediction and highlights its potential to improve public health. Topics covered include the use of AI algorithms for early detection of chronic diseases such as diabetes and cancer and the use of edge computing in wearable devices to detect the spread of infectious diseases. In addition to discussing the challenges and limitations of Edge AI in early health prediction, this article emphasizes future research directions to address these concerns and the integration with existing healthcare systems and explore the full potential of these technologies in improving public health.

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A Systematic Review on Federated Learning in Medical Image Analysis

Cited by 20 publications

References 86 publications

A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques

A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques

A Federated Learning Framework for Brain Tumor Segmentation Without Sharing Patient Data

Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions

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