Table 1 Characteristics of included studies
Authorship | Year | Title | Scientific Journal | Link | Number of Models | Number of Comparisons | Clinical Application | Clinical Domain | Data Type |
|---|---|---|---|---|---|---|---|---|---|
Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto et al. | 2024 | FedER: Federated Learning through Experience Replay and privacy-preserving data synthesis | Computer Vision and Image Understanding | 2 | 5 | Diagnosis | Oncology, Other Respiratory | Dermoscopic images, X-Ray | |
João Coutinho-Almeida, Ricardo João Cruz-Correia, Pedro Pereira Rodrigues | 2024 | Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models | Scientific Reports | 1 | 4 | Prediction | Others | Electronic Health Records / Text | |
Daniel Truhn, Soroosh Tayebi Arasteh, Oliver Lester Saldanha et al. | 2024 | Encrypted federated learning for secure decentralized collaboration in cancer image analysis | Medical Image Analysis | 2 | 5 | Segmentation | Oncology, Various | MRI + Pathology / Whole slide images | |
Erfan Darzi, Nanna M. Sijtsema, P.M.A van Ooijen | 2024 | A Comparative Study of Federated Learning Methods for COVID-19 Detection | Scientific Reports | 5 | 40 | Diagnosis | COVID-19 | CT Scan | |
Lei Bai, Dongang Wang, Hengrui Wang et al. | 2024 | Improving multiple sclerosis lesion segmentation across clinical sites: A federated learning approach with noise-resilient training | Artificial Intelligence in Medicine | 7 | 96 | Segmentation + Classification | Neurology | MRI | |
Angela Mitrovska, Pooyan Safari, Kerstin Ritter et al. | 2024 | Secure federated learning for Alzheimer’s disease detection | Frontiers in Aging Neuroscience | 2 | 16 | Diagnosis | Neurology | MRI | |
Junmo Kim, Min Hyuk Lim, Kwangsoo Kim et al. | 2024 | Continual learning framework for a multicenter study with an application to electrocardiogram | BMC Medical Informatics and Decision Making | 16 | 36 | Diagnosis | Cardiology | ECG / EKG | |
Jiacheng Wang, Yueming Jin, Danail Stoyanov et al. | 2024 | FedDP: Dual Personalization in Federated Medical Image Segmentation | IEEE Transactions on Medical Imaging | 18 | 180 | Segmentation | Gastric & Digestive, Neurology | Endoscopic image, Retina fundus image | |
Yuning Yang, Xiaohong Liu, Tianrun Gao et al. | 2024 | Dense Contrastive-based Federated Learning for Dense Prediction Tasks on Medical Images | IEEE Journal of Biomedical and Health Informatics | 5 | 30 | Diagnosis | Oncology | CT Scan | |
Hussain Alsalman, Mabrook S. Al-Rakhami, Taha Alfakih et al. | 2024 | Federated Learning Approach for Breast Cancer Detection Based on DCNN | IEEE Access | 1 | 6 | Diagnosis | Oncology | Mammography | |
Dipanjali Kundu, Mahbubur Rahman, Anichur Rahman et al. | 2024 | Federated Deep Learning for Monkeypox Disease Detection on GAN-Augmented Dataset | IEEE Access | 4 | 8 | Diagnosis | Others | Dermoscopic images | |
Thi Phuoc Van Nguyen, Wencheng Yang, Zhaohui Tang et al. | 2024 | Lightweight federated learning for STIs/HIV prediction | Scientific Reports | 2 | 28 | Prediction | Others | Electronic Health Records / Text | |
Jiaqi Ge, Gaochao Xu, Jianchao Lu et al. | 2024 | FedAGA: A federated learning framework for enhanced inter-client relationship learning | Knowledge-Based Systems | 9 | 18 | Diagnosis | Oncology | Pathology / Whole slide images | |
Haroon Wahab, Irfan Mehmood, Hassan Ugail et al. | 2024 | Federated Deep Learning for Wireless Capsule Endoscopy Analysis: Enabling Collaboration Across Multiple Data Centers for Robust Learning of Diverse Pathologies | Future Generation Computer Systems | 4 | 16 | Diagnosis | Gastric & Digestive | Endoscopic video (Wireless capsule endoscopy) | |
Isaac Adjei-Mensah, Xiaoling Zhang, Isaac Osei Agyemang et al. | 2024 | Cov-Fed: Federated learning-based framework for COVID-19 diagnosis using chest X-ray scans | Engineering Applications of Artificial Intelligence | 1 | 60 | Diagnosis | COVID-19 | X-Ray | |
Gaeun Sung, Eunjeong Park | 2024 | Aggregate and transfer knowledge of functional connectivity of brain for detecting autism spectrum disorder for multi-site research | Biomedical Signal Processing and Control | 2 | 200 | Diagnosis | Psychology & Psychiatry | MRI | |
I. De Falco, A. Della Cioppa, T. Koutny et al. | 2024 | Model-Free-Communication Federated Learning: Framework and application to Precision Medicine | Biomedical Signal Processing and Control | 1 | 42 | Prediction | Diabetes | Other | |
Jong Chan Yeom, Jae Hoon Kim, Young Jae Kim et al. | 2024 | A Comparative Study of Performance Between Federated Learning and Centralized Learning Using Pathological Image of Endometrial Cancer. | Journal of Imaging Informatics in Medicine | 4 | 14 | Segmentation + Classification | Oncology | Pathology / Whole slide images | |
Isaac Shiri, Yazdan Salimi, Nasim Sirjani et al. | 2024 | Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset | Medical Physics | 1 | 10 | Prognosis (including Mortality) | COVID-19 | CT Scan | |
Vi Thi-Tuong Vo, Tae-ho Shin, Hyung-Jeong Yang et al. | 2024 | A comparison between centralized and asynchronous federated learning approaches for survival outcome prediction using clinical and PET data from non-small cell lung cancer patients | Computer Methods and Programs in Biomedicine | 4 | 20 | Prognosis | Oncology | Electronic Health Records / Text, Other | |
Soroosh Tayebi Arasteh, Peter Isfort, Marwin Saehn et al. | 2023 | Collaborative training of medical artificial intelligence models with non-uniform labels | Scientific Reports | 5 | 5 | Diagnosis | Others | X-Ray | |
Quan Nguyen, Hieu H. Pham, Kok-Seng Wong et al. | 2023 | FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource-Constrained Devices Using Divide and Collaborative Training | IEEE Transactions on Network and Service Management | 1 | 1 | Diagnosis | Oncology | Dermoscopic images | |
Xuanang Xu, Hannah H. Deng, Jaime Gateno et al. | 2023 | Federated Multi-Organ Segmentation With Inconsistent Labels | IEEE Transactions on Medical Imaging | 2 | 120 | Segmentation | Various | CT Scan | |
Hasan Kassem, Deepak Alapatt, Pietro Mascagni et al. | 2023 | Federated Cycling (FedCy): Semi-Supervised Federated Learning of Surgical Phases | IEEE Transactions on Medical Imaging | 16 | 88 | Other | Gastric & Digestive | Laparoscopic cholecystectomy videos | |
Shivam Kalra, Junfeng Wen, Jesse C. Cresswell et al. | 2023 | Decentralized federated learning through proxy model sharing | Nature Communications | 14 | 14 | Diagnosis | Gastric & Digestive, Oncology | Endoscopic image, Pathology / Whole slide images | |
Amelia Jiménez-Sánchez, Mickael Tardy, Miguel A. González Ballester et al. | 2023 | Memory-aware curriculum federated learning for breast cancer classification | Computer Methods and Programs in Biomedicine | 1 | 2 | Diagnosis | Oncology | Mammography | |
Nawrin Tabassum, Mustofa Ahmed, Nushrat Jahan Shorna et al. | 2023 | Depression Detection Through Smartphone Sensing: A Federated Learning Approach | International Journal of Interactive Mobile Technologies | 1 | 4 | Diagnosis | Psychology & Psychiatry | Other | |
Xikang Jiang, Jinhui Zhang, Lin Zhang | 2023 | FedRadar: Federated Multi-Task Transfer Learning for Radar-Based Internet of Medical Things | IEEE Transactions on Network and Service Management | 2 | 128 | Diagnosis | Cardiology | ECG / EKG | |
Ivanoe De Falco, Antonio Della Cioppa, Tomas Koutny et al. | 2023 | A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction | Sensors (Basel) | 1 | 15 | Prediction | Diabetes | Other | |
Hassaan Malik, Tayyaba Anees, Ahmad Naeem et al. | 2023 | Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans | Bioengineering | 2 | 42 | Diagnosis, Segmentation | COVID-19 | CT Scan | |
Laëtitia Launet, Yuandou Wang, Adrián Colomer et al. | 2023 | Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions | Applied Sciences | 1 | 18 | Diagnosis | Oncology | Pathology / Whole slide images | |
Miloš Savić, Vladimir Kurbalija, Mihailo Ilić et al. | 2023 | The application of machine learning techniques in prediction of quality of life features for cancer patients | Computer Science and Information Systems | 10 | 132 | Prediction | Oncology | Electronic Health Records / Text | |
Dong Yun Lee, Byungjin Choi, Chungsoo Kim et al. | 2023 | Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study | Journal of Medical Internet Research | 1 | 4 | Prediction | Psychology & Psychiatry | Electronic Health Records / Text | |
Jun Jie Sim, Weizhuang Zhou, Fook Mun Chan et al. | 2023 | CoVnita, an end-to-end privacy-preserving framework for SARS-CoV-2 classification | Scientific Reports | 1 | 60 | Diagnosis | COVID-19 | Genome | |
Martin Baumgartner, Sai Pavan Kumar Veeranki, Dieter Hayn et al. | 2023 | Introduction and Comparison of Novel Decentral Learning Schemes with Multiple Data Pools for Privacy-Preserving ECG Classification | Journal of Healthcare Informatics Research | 2 | 48 | Diagnosis | Cardiology | ECG / EKG | |
Suraj Rajendran, Zhenxing Xu, Weishen Pan et al. | 2023 | Data heterogeneity in federated learning with Electronic Health Records: Case studies of risk prediction for acute kidney injury and sepsis diseases in critical care | PLoS One Digital Health | 6 | Prediction | Nephrology, Systemic | Electronic Health Records / Text & X-Ray | ||
Xing Wu, Jie Pei, Cheng Chen et al. | 2023 | Federated Active Learning for Multicenter Collaborative Disease Diagnosis | IEEE Transactions on Medical Imaging | 2 | 40 | Diagnosis | COVID-19, Gastric & Digestive | CT Scan, Endoscopic image | |
Weiping Ding, Mohamed Abdel-Basset, Hossam Hawash et al. | 2023 | Generalizable Segmentation of COVID-19 Infection From Multi-Site Tomography Scans: A Federated Learning Framework | IEEE Transactions on Emerging Topics in Computational Intelligence | 1 | 10 | Segmentation | COVID-19 | CT Scan | |
Sarah Haggenmüller, Max Schmitt, Eva Krieghoff-Henning et al. | 2023 | Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics | JAMA Dermatology | 4 | 80 | Diagnosis | Oncology | Pathology / Whole slide images | |
Weiping Ding, Mohamed Abdel-Basset, Hossam Hawash et al. | 2023 | MIC-Net: A deep network for cross-site segmentation of COVID-19 infection in the fog-assisted IoMT | Information Sciences | 1 | 18 | Segmentation | COVID-19 | CT Scan | |
Abhejit Rajagopal, Ekaterina Redekop, Anil Kemisetti et al. | 2023 | Federated Learning with Research Prototypes: Application to Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology | Academic Radiology | 4 | 16 | Segmentation, Diagnosis | Oncology | MRI + Pathology / Whole slide images | |
Mehmet Nergiz | 2023 | Federated learning-based colorectal cancer classification by convolutional neural networks and general visual representation learning | International Journal of Imaging Systems and Technology (IMA) | 14 | 84 | Diagnosis | Oncology | Pathology / Whole slide images | |
Dongnan Liu, Mariano Cabezas, Donggang Wang et al. | 2023 | Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning | Frontiers in Neuroscience | 5 | 80 | Segmentation | Neurology | MRI | |
Soroosh Tayebi Arasteh, Christiane Kuhl, Marwin-Jonathan Saehn et al. | 2023 | Enhancing domain generalization in the AI-based analysis of chest radiographs with federated learning | Scientific Reports | 2 | 120 | Diagnosis | Other Respiratory | X-Ray | |
Matthis Manthe, Stefan Duffner, Carole Lartizien | 2023 | Federated brain tumor segmentation: An extensive benchmark | Medical Image Analysis | https://www.sciencedirect.com/science/article/pii/S1361841524001956 | 17 | 105 | Segmentation | Oncology | MRI |
Zahra Tabatabaei, Yuandou Wang, Adrián Colomer et al. | 2023 | WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval | Bioengineering | 2 | 24 | Diagnosis | Oncology | Pathology / Whole slide images | |
Geun Hyeong Lee, Jonggul Park, Jihyeong Kim et al. | 2023 | Feasibility Study of Federated Learning on the Distributed Research Network of OMOP Common Data Model | Healthcare Informatics Research | 1 | 6 | Prediction | Others | Electronic Health Records / Text | |
Raghdah Saemaldahr, Mohammad Ilyas | 2023 | Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning | Sensors | 4 | 24 | Prediction | Neurology | EEG, Other | |
Faizan Ullah, Muhammad Nadeem, Mohammad Abrar et al. | 2023 | Enhancing Brain Tumor Segmentation Accuracy through Scalable Federated Learning with Advanced Data Privacy and Security Measures | Mathematics | 1 | 15 | Segmentation + Classification | Neurology | MRI | |
Tapotosh Ghosh, Md Istakiak Adnan Palash, Mohammad Abu Yousuf et al. | 2023 | A Robust Distributed Deep Learning Approach to Detect Alzheimer’s Disease from MRI Images | Mathematics | 1 | 48 | Diagnosis | Neurology | MRI | |
Wu-Chun Chung, Yan-Hui Lin, Sih-Han Fang | 2023 | FedISM: Enhancing Data Imbalance via Shared Model in Federated Learning | Mathematics | 4 | 44 | Diagnosis | COVID-19 | X-Ray | |
Giovanni Paragliola, Patrizia Ribino, Zaib Ullah | 2023 | A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network | Journal of Sensor and Actuator Networks | 1 | 26 | Prediction | COVID-19 | EEG | |
Chengxiao Yan, Xiaoyang Zeng, Rui Xi et al. | 2023 | PLA - A Privacy-Embedded Lightweight and Efficient Automated Breast Cancer Accurate Diagnosis Framework for the Internet of Medical Things | Electronics | 1 | 16 | Diagnosis | Oncology | Pathology / Whole slide images | |
Maryum Butt, Noshina Tariq, Muhammad Ashraf et al. | 2023 | A Fog-Based Privacy-Preserving Federated Learning System for Smart Healthcare Applications | Electronics | 1 | 12 | Diagnosis | Other Respiratory | X-Ray | |
Liuyan Yang, Juanjuan He, Yue Fu et al. | 2023 | Federated Learning for Medical Imaging Segmentation via Dynamic Aggregation on Non-IID Data Silos | Electronics | 11 | 572 | Segmentation + Classification | COVID-19 | CT Scan | |
Chetna Gupta, Vikas Khullar, Nitin Goyal et al. | 2023 | Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram. | Diagnostics | 1 | 8 | Diagnosis | Psychology & Psychiatry | Other | |
Ivar Walskaar, Minh Christian Tran, Ferhat Ozgur Catak | 2023 | A Practical Implementation of Medical Privacy-Preserving Federated Learning Using Multi-Key Homomorphic Encryption and Flower Framework | Cryptography | 2 | 64 | Diagnosis | COVID-19 | X-Ray | |
Mohamed Chetoui, Moulay A. Akhloufi | 2023 | Federated Learning for Diabetic Retinopathy Detection Using Vision Transformers | BioMedInformatics | 2 | 32 | Diagnosis | Neurology | Retina fundus image | |
Telmo Baptista, Carlos Soares, Tiago Oliveira et al. | 2023 | Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images | Applied Sciences | 5 | 150 | Diagnosis | Neurology | Retina fundus image | |
Ali Akbar Siddique, S. M. Umar Talha, M. Aamir et al. | 2023 | COVID-19 Classification from X-Ray Images: An Approach to Implement Federated Learning on Decentralized Dataset | Computers, Materials & Continua | 6 | 32 | Diagnosis | COVID-19 | X-Ray | |
Kavitha Srinivasasn, Sainath Prasanna, Rohit Midha et al. | 2023 | Federated Learning Framework for IID and Non-IID datasets of Medical Images | EMITTER International Journal of Engineering Technology | 3 | 4 | Diagnosis, Segmentation | Neurology, Other Respiratory | CT Scan, X-Ray | |
Dhanunjay Potti, Mandavalli N V Saisandeep, V Madhu Viswanatham et al. | 2023 | Heart Stroke Prediction Using Federated Learning | International Journal of Membrane Science and Technology | 2 | 8 | Prediction | Cardiology | Electronic Health Records / Text | |
Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu et al. | 2023 | Federated Partially Supervised Learning With Limited Decentralized Medical Images | IEEE Transactions on Medical Imaging | 8 | 277 | Diagnosis | Other Respiratory | X-Ray | |
Qingguo Zhou, Rui Zhao, Yilin Hu et al. | 2023 | Hierarchical Hybrid Networks for Automatic Pulmonary Blood Vessel Segmentation in Computed Tomography Images | IEEE/ACM Transactions on Computational Biology and Bioinformatics | 1 | 5 | Segmentation | Other Respiratory | CT Scan | |
Ziyuan Yang, Yingyu Chen, Huijie Huangfu et al. | 2023 | Dynamic Corrected Split Federated Learning With Homomorphic Encryption for U-Shaped Medical Image Networks | IEEE Journal of Biomedical and Health Informatics | 9 | 36 | Segmentation | Cardiology | MRI | |
Adriana Anido-Alonso, Diego Alvarez-Estevez | 2023 | Decentralized Data-Privacy Preserving Deep-Learning Approaches for Enhancing Inter-Database Generalization in Automatic Sleep Staging | IEEE Journal of Biomedical and Health Informatics | 2 | 72 | Diagnosis | Others | Other | |
Bless Lord Y Agbley, Jian Ping Li, Amin Ul Haq et al. | 2023 | Federated Fusion of Magnified Histopathological Images for Breast Tumor Classification in the Internet of Medical Things | IEEE Journal of Biomedical and Health Informatics | 2 | 24 | Diagnosis | Oncology | Pathology / Whole slide images | |
Andrew A S Soltan, Anshul Thakur, Jenny Yang et al. | 2023 | A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals | The Lancet Digit Health | 2 | 56 | Diagnosis | COVID-19 | Electronic Health Records / Text | |
Bastian Pfeifer, Hryhorii Chereda, Roman Martin et al. | 2023 | Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification | Bioinformatics | 2 | 4 | Prediction | Oncology | Genome | |
Soroosh Tayebi Arasteh, Christiane Kuhl, Marwin-Jonathan Saehn et al. | 2023 | Enhancing domain generalization in the AI-based analysis of chest radiographs with federated learning | Scientific Reports | 10 | 110 | Diagnosis | Various | X-Ray | |
Daniele Raimondi, Haleh Chizari, Nora Verplaetse et al. | 2023 | Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn’s disease patients | Scientific Reports | 5 | 180 | Diagnosis | Gastric & Digestive | Genome | |
Ruijie Tang, Hengrui Liang, Yuchen Guo et al. | 2023 | Pan-mediastinal neoplasm diagnosis via nationwide federated learning: a multicentre cohort study | The Lancet Digit Health | 1 | 30 | Segmentation + Classification | Oncology | CT Scan | |
Weishen Pan, Zhenxing Xu, Suraj Rajendran et al. | 2023 | An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals | Patterns | 4 | 72 | Prediction | Systemic, Nephrology | Electronic Health Records / Text | |
Bruno Casella, Walter Riviera, Marco Aldinucci et al. | 2023 | MERGE: A model for multi-input biomedical federated learning | Patterns | 2 | 54 | Diagnosis | COVID-19, Neurology | X-Ray, MRI | |
Benteng Ma, Yu Feng, Geng Chen et al. | 2023 | Federated adaptive reweighting for medical image classification | Pattern Recognition | 6 | 72 | Diagnosis | Others | Dermoscopic images, X-Ray | |
Judith Sáinz-Pardo Díaz, Álvaro López García | 2023 | Study of the performance and scalability of federated learning for medical imaging with intermittent clients | Neurocomputing | 1 | 3 | Diagnosis | Other Respiratory | X-Ray | |
Amer Kareem, Haiming Liu, Vladan Velisavljevic | 2023 | A federated learning framework for pneumonia image detection using distributed data | Healthcare Analytics | 5 | 15 | Diagnosis | Other Respiratory | X-Ray | |
Alberto Archetti, Francesca Ieva, Matteo Matteucci | 2023 | Scaling survival analysis in healthcare with federated survival forests: A comparative study on heart failure and breast cancer genomics | Future Generation Computer Systems | 22 | 22 | Prognosis (including Mortality) | Cardiology, Oncology | Genome | |
William Hoyos, Jose Aguilar, Mauricio Toro | 2023 | Federated learning approaches for fuzzy cognitive maps to support clinical decision-making in dengue | Engineering Applications of Artificial Intelligence | 6 | 30 | Prediction, Therapy | Others | Electronic Health Records / Text | |
Isaac Shiri, Behrooz Razeghi, Alireza Vafaei Sadr et al. | 2023 | Multi-institutional PET/CT image segmentation using federated deep transformer learning | Computer Methods and Programs in Biomedicine | 7 | 91 | Segmentation | Oncology | Other | |
Pascal Riedel, Reinhold von Schwerin, Daniel Schaudt et al. | 2023 | ResNetFed: Federated Deep Learning Architecture for Privacy-Preserving Pneumonia Detection from COVID-19 Chest Radiographs | Journal of Healthcare Informatics Research | 1 | 3 | Diagnosis | COVID-19 | X-Ray | |
Wei-Kai Lee, Jia-Sheng Hong, Yi-Hui Lin et al. | 2023 | Federated Learning: A Cross-Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery | Journal of Magnetic Resonance Imaging | 1 | 30 | Segmentation | Oncology | MRI | |
Yuan Yang, Lin Zhang, Lei Ren et al. | 2023 | Distributed autoencoder classifier network for small-scale and scattered COVID-19 dataset classification | International Journal of Imaging Systems and Technology | 2 | 30 | Diagnosis | COVID-19 | CT Scan | |
Bo Guan, Lei Yu, Yang Li et al. | 2023 | Assessment of patients with Parkinson’s disease based on federated learning | International Journal of Machine Learning and Cybernetics | 1 | 1 | Other | Neurology | Other | |
Wided Moulahi, Imen Jdey, Tarek Moulahi et al. | 2023 | A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data | Computers in Biology and Medicine | 1 | 3 | Prediction | Diabetes | Electronic Health Records / Text | |
Miao Zhang, Liangqiong Qu, Praveer Singh et al. | 2022 | SplitAVG: A Heterogeneity-Aware Federated Deep Learning Method for Medical Imaging | IEEE Journal of Biomedical and Health Informatics | 38 | 78 | Segmentation + Classification, Prediction, Diagnosis, Segmentation | Diabetes, Others, Oncology, Neurology | Retina fundus image, X-Ray, MRI | |
Tien-Dung Cao, Tram Truong-Huu, Hien Tran et al. | 2022 | A federated deep learning framework for privacy preservation and communication efficiency | Journal of Systems Architecture | 1 | 8 | Diagnosis | Neurology | MRI | |
Ahmed Sleem, Ibrahim Elhenawy | 2022 | Collaborative Segmentation of COVID-19 From non-IID Topographies in the Internet of Medical Things (IoMT) | Journal of Intelligent Systems & Internet of Things | 3 | 24 | Segmentation | COVID-19 | CT Scan | |
Yawen Wu, Dewen Zeng, Zhepeng Wang et al. | 2022 | Distributed contrastive learning for medical image segmentation | Medical Image Analysis | 4 | 4 | Segmentation | Cardiology | MRI | |
Praveen Joshi, Chandra Thapa, Seyit Camtepe et al. | 2022 | Performance and Information Leakage in Splitfed Learning and Multi-Head Split Learning in Healthcare Data and Beyond | Methods Protoc | 4 | 4 | Diagnosis | Various | ECG / EKG, Dermoscopic images | |
Mohamed Abdel-Basset, Hossam Hawash, Mohamed Abouhawwash | 2022 | Collaborative Screening of COVID-19-like Disease from Multi-Institutional Radiographs: A Federated Learning Approach | Mathematics | 2 | 36 | Segmentation | COVID-19 | CT Scan | |
Lucian Mihai Florescu, Costin Teodor Streba, Mircea-Sebastian Şerbănescu et al. | 2022 | Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images | Life (Basel) | 1 | 5 | Diagnosis | COVID-19 | CT Scan | |
Lingxiao Li, Niantao Xie, Sha Yuan | 2022 | A Federated Learning Framework for Breast Cancer Histopathological Image Classification | Electronics | 4 | 20 | Diagnosis | Oncology | Pathology / Whole slide images | |
Tingyang Yang, Jingshuang Xu, Mengxiao Zhu et al. | 2022 | FedZaCt: Federated Learning with Z Average and Cross-Teaching on Image Segmentation | Electronics | 23 | 76 | Segmentation | Others, Various | Unstated, Dermoscopic images | |
Bless Lord Y. Agbley, Jianping Li, Md Altab Hossin et al. | 2022 | Federated Learning-Based Detection of Invasive Carcinoma of No Special Type with Histopathological Images | Diagnostics | 1 | 48 | Diagnosis | Oncology | Pathology / Whole slide images | |
Songshang Liu, Howard H. Yang, Yiqi Tao et al. | 2022 | Privacy-Preserved Federated Learning for 3D Tooth Segmentation in Intra-Oral Mesh Scans | Frontiers in Communications and Networks | 1 | 36 | Segmentation | Others | Intra-Oral Mesh Scans | |
Barkha Kakkar, Prashant Johri, Yogesh Kumar et al. | 2022 | An IoMT-Based Federated and Deep Transfer Learning Approach to the Detection of Diverse Chest Diseases Using Chest X-Rays | Human-centric Computing and Information Sciences | 5 | 15 | Diagnosis | Other Respiratory | Electronic Health Records / Text & X-Ray | |
Geun Hyeong Lee, Soo-Yong Shin | 2022 | Federated Learning on Clinical Benchmark Data: Performance Assessment | JMIR Medical Informatics | 2 | 34 | Mortality, Diagnosis | Others, Cardiology | Electronic Health Records / Text, ECG / EKG | |
T. V. Nguyen, M. A. Dakka, S. M. Diakiw et al. | 2022 | A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data | Scientific Reports | 2 | 2 | Diagnosis | Others | Microscopy | |
Mohammed Adnan, Shivam Kalra, Jesse C. Cresswell et al. | 2022 | Federated learning and differential privacy for medical image analysis | Scientific Reports | 2 | 24 | Diagnosis | Oncology | Pathology / Whole slide images | |
Omneya Atef, Mustafa Abdul Salam, Hisham Abdelsalam | 2022 | Federated Learning Approach for Measuring the Response of Brain Tumors to Chemotherapy | International Journal of Advanced Computer Science and Applications | 1 | 2 | Prognosis | Oncology | MRI | |
Dhurgham Hassan Mahlool, Mohamed Hamzah Abed | 2022 | Distributed brain tumor diagnosis using a federated learning environment | Bulletin of Electrical Engineering and Informatics | 1 | 8 | Diagnosis | Oncology | MRI | |
Ziyu Wang, Lei Cai, Xuewu Zhang et al. | 2022 | A COVID-19 Auxiliary Diagnosis Based on Federated Learning and Blockchain | Computational and Mathematical Methods in Medicine | 2 | 20 | Diagnosis | COVID-19 | CT Scan | |
Xiaolong Xu, Hao Tian, Xuyun Zhang et al. | 2022 | DisCOV: Distributed COVID-19 Detection on X-Ray Images With Edge-Cloud Collaboration | IEEE Transactions on Services Computing | 1 | 3 | Diagnosis | COVID-19 | X-Ray | |
Thang Ngo, Dinh C. Nguyen, Pubudu N. Pathirana et al. | 2022 | Federated Deep Learning for the Diagnosis of Cerebellar Ataxia: Privacy Preservation and Auto-Crafted Feature Extractor | IEEE Transactions on Neural Systems and Rehabilitation Engineering | 1 | 1 | Diagnosis | Neurology | Electronic Health Records / Text | |
Ling-Li Zeng, Zhipeng Fan, Jianpo Su et al. | 2022 | Gradient Matching Federated Domain Adaptation for Brain Image Classification | IEEE Transactions on Neural Networks and Learning Systems | 6 | 90 | Diagnosis | Psychology & Psychiatry | MRI | |
Zheyao Gao, Fuping Wu, Weiguo Gao et al. | 2022 | A New Framework of Swarm Learning Consolidating Knowledge From Multi-Center Non-IID Data for Medical Image Segmentation | IEEE Transactions on Medical Imaging | 17 | 85 | Segmentation | Cardiology, Others, Oncology | MRI, CT Scan | |
Liang Zou, Zexin Huang, Xinhui Yu et al. | 2022 | Automatic Detection of Congestive Heart Failure Based on Multiscale Residual UNet + +: From Centralized Learning to Federated Learning | IEEE Transactions on Instrumentation and Measurement | 1 | 27 | Diagnosis | Cardiology | ECG / EKG | |
Jeffry Wicaksana, Zengqiang Yan, Xin Yang et al. | 2022 | Customized Federated Learning for Multi-Source Decentralized Medical Image Classification | IEEE Journal of Biomedical and Health Informatics | 4 | 16 | Diagnosis | Oncology | MRI, Dermoscopic images | |
Amin Aminifar, Matin Shokri, Fazle Rabbi et al. | 2022 | Extremely Randomized Trees With Privacy Preservation for Distributed Structured Health Data | IEEE Access | 8 | 128 | Diagnosis | Cardiology, Oncology, Psychology & Psychiatry | Other, Pathology / Whole slide images | |
Oliver Lester Saldanha, Philip Quirke, Nicholas P. West et al. | 2022 | Swarm learning for decentralized artificial intelligence in cancer histopathology | Nature Medicine | 3 | 27 | Prediction | Oncology | Pathology / Whole slide images | |
Brendon Lutnick, David Manthey, Jan U. Becker et al. | 2022 | A tool for federated training of segmentation models on whole slide images | Journal of Pathology Informatics | 1 | 6 | Segmentation | Nephrology | Pathology / Whole slide images | |
Isaac Shiri, Alireza Vafaei Sadr, Mehdi Amini et al. | 2022 | Decentralized Distributed Multi-institutional PET Image Segmentation Using a Federated Deep Learning Framework | Clinical Nuclear Medicine | 1 | 54 | Segmentation | Oncology | PET image | |
Le Peng, Gaoxiang Luo, Andrew Walker et al. | 2022 | Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals | Journal of the American Medical Informatics Association | 4 | 41 | Diagnosis | COVID-19 | X-Ray | |
Akis Linardos, Kaisar Kushibar, Sean Walsh et al. | 2022 | Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease | Scientific Reports | 11 | 83 | Diagnosis | Cardiology | MRI | |
Mahbubur Rahman, Dipanjali Kundu, Sayma Alam Suha et al. | 2022 | Hospital patients’ length of stay prediction: A federated learning approach | Journal of King Saud University - Computer and Information Sciences | 3 | 90 | Prognosis | Various | Electronic Health Records / Text | |
Dianbo Liu, Kathe Fox, Griffin Weber et al. | 2022 | Confederated learning in healthcare: Training machine learning models using disconnected data separated by individual, data type and identity for Large-Scale health system Intelligence | Journal of Biomedical Informatics | 6 | 24 | Prediction | Diabetes, Psychology & Psychiatry, Cardiology | Electronic Health Records / Text | |
Giovanni Paragliola, Antonio Coronato | 2022 | Definition of a novel federated learning approach to reduce communication costs | Expert Systems with Applications | 3 | 25 | Prediction | Systemic | ECG / EKG | |
Rajesh Kumar, Jay Kumar, Abdullah Aman Khan et al. | 2022 | Blockchain and homomorphic encryption based privacy-preserving model aggregation for medical images | Computerized Medical Imaging and Graphics | 6 | 45 | Diagnosis, Segmentation | COVID-19 | CT Scan | |
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Mustafa Abdul Salam, Sanaa Taha, Mohamed Ramadan | 2021 | COVID-19 detection using federated machine learning | PLoS One | 5 | 12 | Prognosis, Diagnosis | COVID-19 | Electronic Health Records / Text, X-Ray | |
Xiaodong Wang, Zhen’an He, Ying Wang et al. | 2021 | An Intestinal Centerline Extraction Algorithm Based on Federated Framework | Wireless Communications and Mobile Computing | 2 | 6 | Segmentation | Gastric & Digestive | CT Scan | |
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Qi Dou, Tiffany Y. So, Meirui Jiang et al. | 2021 | Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study | npj Digital Medicine | 1 | 48 | Diagnosis | COVID-19 | CT Scan | |
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Julian Lo, Timothy T. Yu, Da Ma et al. | 2021 | Federated Learning for Microvasculature Segmentation and Diabetic Retinopathy Classification of OCT Data | Ophthalmology Science | 1 | 80 | Segmentation + Classification | Diabetes | OCTA + OCT | |
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Xiaoxiao Li, Yufeng Gu, Nicha Dvornek et al. | 2020 | Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results | Medical Image Analysis | 4 | 44 | Diagnosis | Psychology & Psychiatry | MRI | |
M. Lincy, Dr. A. Meena Kowshalya | 2020 | Early Detection of Type-2 Diabetes Using Federated Learning | International Journal of Scientific Research in Science Engineering and Technology | 3 | 6 | Diagnosis | Diabetes | Unstated | |
Mohammed Alawad, Hong-Jun Yoon, Shang Gao et al. | 2020 | Privacy-Preserving Deep Learning NLP Models for Cancer Registries | IEEE Transactions on Emerging Topics in Computational Intelligence | 3 | 32 | Segmentation, Segmentation + Classification | COVID-19, Oncology | CT Scan, Electronic Health Records / Text | |
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Elena Czeizler, Wolfgang Wiessler, Thorben Koester et al. | 2020 | Using federated data sources and Varian Learning Portal framework to train a neural network model for automatic organ segmentation | Physica Medica | 3 | 6 | Segmentation | Nephrology, Gastric & Digestive, Others | CT Scan | |
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Li Huang, Andrew L. Shea, Huining Qian et al. | 2019 | Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records | Journal of Biomedical Informatics | 10 | 20 | Prognosis (including Mortality) | Systemic | Electronic Health Records / Text | |
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Arthur Jochems, Timo M. Deist, Issam El Naqa et al. | 2017 | Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries | International Journal of Radiation Oncology*Biology*Physics | 1 | 1 | Mortality | Oncology | Electronic Health Records / Text | |
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