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Agbley BLY, Li JP, Haq AU, Bankas EK, Mawuli CB, Ahmad S, Khan S, Khan AR. Federated Fusion of Magnified Histopathological Images for Breast Tumor Classification in the Internet of Medical Things. IEEE J Biomed Health Inform 2024; 28:3389-3400. [PMID: 37028353 DOI: 10.1109/jbhi.2023.3256974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Breast tumor detection and classification on the Internet of Medical Things (IoMT) can be automated with the potential of Artificial Intelligence (AI). Deep learning models rely on large datasets, however, challenges arise when dealing with sensitive medical data. Restrictions on sharing these medical data result in limited publicly available datasets thereby impacting the performance of the deep learning models. To address this issue, we propose an approach that combines different magnification factors of histopathological images using a residual network and information fusion in Federated Learning (FL). FL is employed to preserve the privacy of patient data, while enabling the creation of a global model. Using the BreakHis dataset, we compare the performance of FL with centralized learning (CL). We also performed visualizations for explainable AI. The final models obtained become available for deployment on internal IoMT systems in healthcare institutions for timely diagnosis and treatment. Our results demonstrate that the proposed approach outperforms existing works in the literature on multiple metrics.
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Haq AU, Li JP, Khan I, Agbley BLY, Ahmad S, Uddin MI, Zhou W, Khan S, Alam I. DEBCM: Deep Learning-Based Enhanced Breast Invasive Ductal Carcinoma Classification Model in IoMT Healthcare Systems. IEEE J Biomed Health Inform 2024; 28:1207-1217. [PMID: 37015704 DOI: 10.1109/jbhi.2022.3228577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Accurate breast cancer (BC) diagnosis is a difficult task that is critical for the proper treatment of BC in IoMT (Internet of Medical Things) healthcare systems. This paper proposes a convolutional neural network (CNN)-based diagnosis method for detecting early-stage breast cancer. In developing the proposed method, we incorporated the CNN model for the invasive ductal carcinoma (IDC) classification using breast histology image data. We have incorporated transfer learning (TL) and data augmentation (DA) mechanisms to improve the CNN model's predictive outcomes. For the fine-tuning process, the CNN model was trained with breast histology image data. Furthermore, the held-out cross-validation method for best model selection and hyper-parameter tuning was incorporated. In addition, various performance evaluation metrics for model performance assessment were computed. The experimental results confirmed that the proposed model outperformed the baseline models across all evaluation metrics, achieving 99.04% accuracy. We recommend the proposed method for early recognition of BC in IoMT healthcare systems due to its high performance.
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Gupta C, Khullar V, Goyal N, Saini K, Baniwal R, Kumar S, Rastogi R. Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram. Diagnostics (Basel) 2023; 14:43. [PMID: 38201350 PMCID: PMC10795654 DOI: 10.3390/diagnostics14010043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/12/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
In this day and age, depression is still one of the biggest problems in the world. If left untreated, it can lead to suicidal thoughts and attempts. There is a need for proper diagnoses of Major Depressive Disorder (MDD) and evaluation of the early stages to stop the side effects. Early detection is critical to identify a variety of serious conditions. In order to provide safe and effective protection to MDD patients, it is crucial to automate diagnoses and make decision-making tools widely available. Although there are various classification systems for the diagnosis of MDD, no reliable, secure method that meets these requirements has been established to date. In this paper, a federated deep learning-based multimodal system for MDD classification using electroencephalography (EEG) and audio datasets is presented while meeting data privacy requirements. The performance of the federated learning (FL) model was tested on independent and identically distributed (IID) and non-IID data. The study began by extracting features from several pre-trained models and ultimately decided to use bidirectional short-term memory (Bi-LSTM) as the base model, as it had the highest validation accuracy of 91% compared to a convolutional neural network and LSTM with 85% and 89% validation accuracy on audio data, respectively. The Bi-LSTM model also achieved a validation accuracy of 98.9% for EEG data. The FL method was then used to perform experiments on IID and non-IID datasets. The FL-based multimodal model achieved an exceptional training and validation accuracy of 99.9% when trained and evaluated on both IID and non-IIID datasets. These results show that the FL multimodal system performs almost as well as the Bi-LSTM multimodal system and emphasize its suitability for processing IID and non-IIID data. Several clients were found to perform better than conventional pre-trained models in a multimodal framework for federated learning using EEG and audio datasets. The proposed framework stands out from other classification techniques for MDD due to its special features, such as multimodality and data privacy for edge machines with limited resources. Due to these additional features, the framework concept is the most suitable alternative approach for the early classification of MDD patients.
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Affiliation(s)
- Chetna Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140417, Punjab, India; (C.G.); (V.K.)
| | - Vikas Khullar
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140417, Punjab, India; (C.G.); (V.K.)
| | - Nitin Goyal
- Department of Computer Science and Engineering, School of Engineering and Technology, Central University of Haryana, Mahendergarh 123031, Haryana, India;
| | - Kirti Saini
- Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra 136119, Haryana, India;
| | - Ritu Baniwal
- Department of Computer Science, Jyotiba Phule Government College, Radaur, Yamunanagar 135133, Haryana, India;
| | - Sushil Kumar
- Department of Computer Science and Engineering, School of Engineering and Technology, Central University of Haryana, Mahendergarh 123031, Haryana, India;
| | - Rashi Rastogi
- Department of Computer Applications, Sir Chottu Ram Institute of Engineering & Technology, Ch. Charan Singh University, Meerut 250001, Uttar Pradesh, India;
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Sandhu SS, Gorji HT, Tavakolian P, Tavakolian K, Akhbardeh A. Medical Imaging Applications of Federated Learning. Diagnostics (Basel) 2023; 13:3140. [PMID: 37835883 PMCID: PMC10572559 DOI: 10.3390/diagnostics13193140] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 10/03/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023] Open
Abstract
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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Affiliation(s)
- Sukhveer Singh Sandhu
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA; (H.T.G.); (P.T.)
| | - Hamed Taheri Gorji
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA; (H.T.G.); (P.T.)
- SafetySpect Inc., 4200 James Ray Dr., Grand Forks, ND 58202, USA
| | - Pantea Tavakolian
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA; (H.T.G.); (P.T.)
| | - Kouhyar Tavakolian
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA; (H.T.G.); (P.T.)
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Aneja S, Avesta A, Xu H, Machado LO. Clinical Informatics Approaches to Facilitate Cancer Data Sharing. Yearb Med Inform 2023; 32:104-110. [PMID: 37414028 PMCID: PMC10751108 DOI: 10.1055/s-0043-1768721] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/08/2023] Open
Abstract
OBJECTIVES Despite growing enthusiasm surrounding the utility of clinical informatics to improve cancer outcomes, data availability remains a persistent bottleneck to progress. Difficulty combining data with protected health information often limits our ability to aggregate larger more representative datasets for analysis. With the rise of machine learning techniques that require increasing amounts of clinical data, these barriers have magnified. Here, we review recent efforts within clinical informatics to address issues related to safely sharing cancer data. METHODS We carried out a narrative review of clinical informatics studies related to sharing protected health data within cancer studies published from 2018-2022, with a focus on domains such as decentralized analytics, homomorphic encryption, and common data models. RESULTS Clinical informatics studies that investigated cancer data sharing were identified. A particular focus of the search yielded studies on decentralized analytics, homomorphic encryption, and common data models. Decentralized analytics has been prototyped across genomic, imaging, and clinical data with the most advances in diagnostic image analysis. Homomorphic encryption was most often employed on genomic data and less on imaging and clinical data. Common data models primarily involve clinical data from the electronic health record. Although all methods have robust research, there are limited studies showing wide scale implementation. CONCLUSIONS Decentralized analytics, homomorphic encryption, and common data models represent promising solutions to improve cancer data sharing. Promising results thus far have been limited to smaller settings. Future studies should be focused on evaluating the scalability and efficacy of these methods across clinical settings of varying resources and expertise.
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Affiliation(s)
- Sanjay Aneja
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation at Yale, New Haven, CT, USA
- Department of Bioinformatics and Data Science, Yale School of Medicine, New Haven, CT, USA
| | - Arman Avesta
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation at Yale, New Haven, CT, USA
| | - Hua Xu
- Department of Bioinformatics and Data Science, Yale School of Medicine, New Haven, CT, USA
| | - Lucila Ohno Machado
- Department of Bioinformatics and Data Science, Yale School of Medicine, New Haven, CT, USA
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Nazir S, Kaleem M. Federated Learning for Medical Image Analysis with Deep Neural Networks. Diagnostics (Basel) 2023; 13:diagnostics13091532. [PMID: 37174925 PMCID: PMC10177193 DOI: 10.3390/diagnostics13091532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 04/14/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-art performance in image classification and segmentation tasks, aiding disease diagnosis. The accuracy of the DNN is largely governed by the quality and quantity of the data used to train the model. However, for the medical images, the critical security and privacy concerns regarding sharing of local medical data across medical establishments precludes exploiting the full DNN potential for clinical diagnosis. The federated learning (FL) approach enables the use of local model's parameters to train a global model, while ensuring data privacy and security. In this paper, we review the federated learning applications in medical image analysis with DNNs, highlight the security concerns, cover some efforts to improve FL model performance, and describe the challenges and future research directions.
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Affiliation(s)
- Sajid Nazir
- Department of Computing, Glasgow Caledonian University, Glasgow G4 0BA, UK
| | - Mohammad Kaleem
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 45550, Pakistan
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