Federated learning for healthcare applications

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works

Regression Models and Life-Tables

Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793 · see citing works

MIMIC-III, a freely accessible critical care database

Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141 · see citing works

Advances and Open Problems in Federated Learning

Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772 · cited 1× · see citing works

The multimodal brain tumor image segmentation benchmark (BRATS)

Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781 · see citing works

A Comprehensive Survey on Transfer Learning

Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3004555 · Cited by 6,678 · see citing works

Curriculum learning

Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 1× · see citing works

Image Quality Metrics: PSNR vs. SSIM

Hore, A., & Ziou, D. (2010) · 2010 20th International Conference on Pattern Recognition · doi:10.1109/icpr.2010.579 · Cited by 4,943 · see citing works

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842 · see citing works

ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616 · see citing works

The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

Tschandl, P., Rosendahl, C., & Kittler, H. (2018) · Scientific Data · doi:10.1038/sdata.2018.161 · Cited by 3,628 · see citing works

Review The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge

Tomczak, K. J., Czerwińska, P., & Wiznerowicz, M. (2015) · Współczesna Onkologia · doi:10.5114/wo.2014.47136 · Cited by 3,503 · see citing works

A Tutorial on the Cross-Entropy Method

Boer, P., Kroese, D. P., Mannor, S., & Rubinstein, R. (2005) · Annals of Operations Research · doi:10.1007/s10479-005-5724-z · Cited by 3,348 · see citing works

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049 · see citing works

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., Anderson, J. S., Assaf, M., Bookheimer, S. Y., Dapretto, M., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.78 · Cited by 2,780 · see citing works

Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)

Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363547 · Cited by 2,620 · see citing works

Automated detection of COVID-19 cases using deep neural networks with X-ray images

Öztürk, T., Talo, M., Yildirim, E. A., Baloğlu, U. B., Yıldırım, Ö., & Acharya, U. R. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103792 · Cited by 2,607 · see citing works

Dataset of breast ultrasound images

Al-Dhabyani, W., Gomaa, M., Khaled, H., & Fahmy, A. (2020) · Data in Brief · doi:10.1016/j.dib.2019.104863 · Cited by 2,405 · see citing works

Knowledge Graphs

Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., de Melo, G., Gutiérrez, C., Kirrane, S., Gayo, J. E. L., Roberto Navigli, A., Neumaier, S., et al. (2021) · ACM Computing Surveys · doi:10.1145/3447772 · Cited by 2,371 · see citing works

A Controlled Trial to Improve Care for Seriously III Hospitalized Patients

Connors, A. F. (1995) · JAMA · doi:10.1001/jama.1995.03530200027032 · Cited by 2,333 · see citing works

Federated Learning with Non-IID Data

Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077 · see citing works

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

You, X., Wang, C., Huang, J., Gao, X., Zhang, Z., Wang, M., Huang, Y., Zhang, C., Jiang, Y., Wang, J., et al. (2020) · Science China Information Sciences · doi:10.1007/s11432-020-2955-6 · Cited by 2,058 · see citing works

DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network

Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0482-1 · Cited by 1,995 · see citing works

A brief introduction to weakly supervised learning

Zhou, Z.-H. (2018) · National Science Review · doi:10.1093/nsr/nwx106 · Cited by 1,899 · see citing works

A survey on federated learning

Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106775 · Cited by 1,899 · see citing works

Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016) · arXiv · doi:10.48550/arxiv.1610.02527 · Cited by 1,563 · see citing works

Federated Learning for Healthcare Informatics

Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-020-00082-4 · Cited by 1,542 · cited 1× · see citing works

Introducing a New Benchmarked Dataset for Activity Monitoring

Reiss, A., & Stricker, D. (2012) · 2012 16th International Symposium on Wearable Computers · doi:10.1109/iswc.2012.13 · Cited by 1,456 · cited 1× · see citing works

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R. T., Berger, C., Ha, S. M., Rozycki, M., et al. (2018) · Apollo - University of Cambridge Repository · doi:10.17863/cam.38755 · Cited by 1,441 · see citing works

On First-Order Meta-Learning Algorithms

Nichol, A., Achiam, J., & Schulman, J. (2018) · arXiv · doi:10.48550/arxiv.1803.02999 · Cited by 1,438 · see citing works

The Liver Tumor Segmentation Benchmark (LiTS)

Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102680 · Cited by 1,412 · see citing works

The Medical Segmentation Decathlon

Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30695-9 · Cited by 1,384 · see citing works

2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text

Uzuner, Ö., South, B. R., Shen, S., & DuVall, S. L. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000203 · Cited by 1,258 · see citing works

Comparison and evaluation of methods for liver segmentation from CT datasets

Heimann, T., van Ginneken, B., Styner, M. A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., et al. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2013851 · Cited by 1,135 · see citing works

Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios

Cascella, M., Montomoli, J., Bellini, V., & Bignami, E. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-01925-4 · Cited by 1,120 · see citing works

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

Twinanda, A. P., Shehata, S., Mutter, D., Marescaux, J., de Mathelin, M., & Padoy, N. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2593957 · Cited by 1,116 · see citing works

Medical SAM Adapter: Adapting Segment Anything Model for Medical Image SegmentationPreprint

Wu, J.-D., Fu, R., Fang, H., et al. (2023) · Medical Image Analysis · doi:10.48550/arxiv.2304.12620 · Cited by 1,091 · see citing works

Automated breast ultrasound lesions detection using convolutional neural networks

Yap, M. H., Pons, G., Martí, J., Ganau, S., Sentís, M., Zwiggelaar, R., Davison, A. K., & Martí, R. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2731873 · Cited by 1,086 · see citing works

Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography

Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.045 · Cited by 917 · see citing works

Contrastive Representation Learning: A Framework and Review

Le-Khac, P. H., Healy, G., & Smeaton, A. F. (2020) · IEEE Access · doi:10.1109/access.2020.3031549 · Cited by 862 · see citing works

Evaluation of prostate segmentation algorithms for mri: The promise12 challenge

Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., et al. (2014) · Medical Image Analysis · doi:10.1016/j.media.2013.12.002 · Cited by 857 · see citing works

Federated learning for predicting clinical outcomes in patients with COVID-19

Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01506-3 · Cited by 842 · see citing works

CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation

Kavur, A. E., Gezer, N. S., BARIŞ, M. M., Aslan, S., Conze, P.-H., Groza, V., Pham, D. D., Chatterjee, S., Ernst, P., Özkan, S., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101950 · Cited by 828 · see citing works

FedProto: Federated Prototype Learning across Heterogeneous Clients

Tan, Y., Long, G., Liu, L., Zhou, T., Lu, Q., Jiang, J. B., & Zhang, C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20819 · Cited by 809 · see citing works

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

Roth, H. R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E. B., & Summers, R. M. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24553-9_68 · Cited by 784 · see citing works

OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease

LaMontagne, P. J., Benzinger, T. L., Morris, J. C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., Vlassenko, A. G., et al. (2019) · medRxiv · doi:10.1101/2019.12.13.19014902 · Cited by 779 · cited 1× · see citing works

nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer

Zhou, H.-Y., Guo, J., Zhang, Y., Han, X., Yu, L., Wang, L., & Yu, Y. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3293771 · Cited by 749 · see citing works

COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images

Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K. N., & Mohammadi, A. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.09.010 · Cited by 710 · see citing works

A large annotated medical image dataset for the development and evaluation of segmentation algorithmsPreprint

Simpson, A. L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K., van Ginneken, B., Kopp‐Schneider, A., Landman, B. A., Litjens, G. J. S., Menze, B. H., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.09063 · Cited by 703 · see citing works

From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

Bandi, P., Geessink, O., Manson, Q., Van Dijk, M., Balkenhol, M., Hermsen, M., Ehteshami Bejnordi, B., Lee, B., Paeng, K., Zhong, A., et al. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2867350 · Cited by 569 · see citing works

A patient-centric dataset of images and metadata for identifying melanomas using clinical context

Rotemberg, V. M., Kurtansky, N. R., Betz‐Stablein, B., Caffery, L. J., Chousakos, E., Codella, N., Combalia, M., Dusza, S. W., Guitera, P., Gutman, D. A., et al. (2021) · Scientific Data · doi:10.1038/s41597-021-00815-z · Cited by 558 · see citing works

MedViT: A robust vision transformer for generalized medical image classification

Manzari, O. N., Ahmadabadi, H., Kashiani, H., Shokouhi, S. B., & Ayatollahi, A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106791 · Cited by 537 · see citing works

What is a good evaluation measure for semantic segmentation?

Csurka, G., Larlus, D., & Perronnin, F. (2013) · British Machine Vision Conference (BMVC) · doi:10.5244/c.27.32 · Cited by 536 · see citing works

Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism

Khan, L. U., Pandey, S. R., Tran, N. H., Saad, W., Han, Z., Nguyen, M. N. H., & Hong, C. S. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900649 · Cited by 506 · see citing works

Fair Resource Allocation in Federated Learning

Li, T., Sanjabi, M., Beirami, A., & Smith, V. (2019) · arXiv · doi:10.48550/arxiv.1905.10497 · Cited by 475 · see citing works

Federated Learning and Differential Privacy for Medical Image Analysis

Adnan, M., Kalra, S., Cresswell, J. C., Taylor, G. W., & Tizhoosh, H. (2021) · doi:10.21203/rs.3.rs-1005694/v1 · Cited by 464 · see citing works

Development of metaverse for intelligent healthcare

Wang, G., Badal, A., Jia, X., Maltz, J. S., Mueller, K. D., Myers, K. J., Niu, C., Vannier, M. W., Yan, P., Yu, Z., et al. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00549-6 · Cited by 430 · see citing works

fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning

Knöll, F., Žbontar, J., Sriram, A., Muckley, M. J., Bruno, M. T., Defazio, A., Parente, M., Geras, K. J., Katsnelson, J., Chandarana, H., et al. (2020) · Radiology Artificial Intelligence · doi:10.1148/ryai.2020190007 · Cited by 420 · see citing works

MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis

Yang, J., Shi, R., & Ni, B. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434062 · Cited by 414 · see citing works

A state-of-the-art survey on solving non-IID data in Federated Learning

Ma, X., Zhu, J., Lin, Z., Chen, S., & Qin, Y. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.05.003 · Cited by 404 · see citing works

CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301590 · Cited by 392 · see citing works

Homomorphic Encryption-Based Privacy-Preserving Federated Learning in IoT-Enabled Healthcare System

Zhang, L., Xu, J., Vijayakumar, P., Sharma, P. K., & Ghosh, U. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3185327 · Cited by 376 · see citing works

A survey on deep learning in medicine: Why, how and when?

Piccialli, F., Somma, V. D., Giampaolo, F., Cuomo, S., & Fortino, G. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.09.006 · Cited by 352 · see citing works

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

Lemaître, G., Martí, R., Freixenet, J., Vilanova, J. C., WALKER, P. M., & Mériaudeau, F. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.02.009 · Cited by 349 · see citing works

MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data

Liu, Q., Dou, Q., Yu, L., & Heng, P. A. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2974574 · Cited by 343 · see citing works

Distributed deep learning networks among institutions for medical imaging

Chang, K., Balachandar, N., Lam, C., Yi, D., Brown, J., Beers, A., Rosen, B., Rubin, D. L., & Kalpathy-Cramer, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy017 · Cited by 340 · see citing works

COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images

Gunraj, H., Wang, L., & Wong, A. M. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.608525 · Cited by 336 · see citing works

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

Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3329061 · Cited by 315 · see citing works

A hybrid solution for privacy preserving medical data sharing in the cloud environment

Yang, J., Li, J., & Niu, Y. (2014) · Future Generation Computer Systems · doi:10.1016/j.future.2014.06.004 · Cited by 299 · see citing works

Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges

Rodríguez-Barroso, N., Jiménez-López, D., Luzón, M. V., Herrera, F., & Martínez-Cámara, E. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.09.011 · Cited by 296 · see citing works

Learn from Others and Be Yourself in Heterogeneous Federated Learning

Huang, W., Ye, M., & Du, B. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00990 · Cited by 289 · see citing works

BCN20000: Dermoscopic Lesions in the Wild

Combalia, M., Codella, N., Rotemberg, V. M., Helba, B., Vilaplana, V., Reiter, O., Carrera, C., Barreiro, A., Halpern, A. C., Puig, S., et al. (2019) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · doi:10.48550/arxiv.1908.02288 · Cited by 285 · see citing works

Segmentation Outside the Cranial Vault Challenge

harrigr (2015) · Synapse · doi:10.7303/syn3193805 · Cited by 257 · see citing works

Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19

Vaid, A., Jaladanki, S. K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., Freitas, J. K. D., Wanyan, T., et al. (2020) · doi:10.1101/2020.08.11.20172809 · Cited by 253 · cited 1× · see citing works

Layer-wised Model Aggregation for Personalized Federated Learning

Ma, X., Zhang, J., Guo, S., & Xu, W. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00985 · Cited by 253 · see citing works

Human activity recognition based on multienvironment sensor data

Li, Y., Yang, G., Su, Z., Li, S., & Wang, Y. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.10.015 · Cited by 251 · see citing works

Recent advances on federated learning: A systematic survey

Liu, B., Lv, N., Guo, Y., & Li, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128019 · Cited by 244 · see citing works

EPILEPSIAE – A European epilepsy database

Ihle, M., Feldwisch‐Drentrup, H., Teixeira, C., Witon, A., Schelter, B. O., Timmer, J., & Schulze‐Bonhage, A. (2010) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2010.08.011 · Cited by 242 · see citing works

Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

Tang, Z., Chuang, K. V., DeCarli, C. S., Jin, L., Beckett, L. A., Keiser, M. J., & Dugger, B. N. (2019) · Nature Communications · doi:10.1038/s41467-019-10212-1 · Cited by 228 · see citing works

Incidence, prevalence, and global burden of autism spectrum disorder from 1990 to 2019 across 204 countries

Solmi, M., Song, M., Yon, D. K., Lee, S. W., Fombonne, E., Kim, M. S., Park, S., Lee, M. H., Hwang, J., Keller, R., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01630-7 · Cited by 226 · see citing works

Classification of Brain Disorders in rs-fMRI via Local-to-Global Graph Neural Networks

Zhang, H., Song, R., Wang, L., Zhang, L., Wang, D., Wang, C., & Zhang, W. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3219260 · Cited by 220 · see citing works

Designing ECG monitoring healthcare system with federated transfer learning and explainable AI

Raza, A., Tran, K. P., Koehl, L., & Li, S. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107763 · Cited by 205 · see citing works

Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning

Guo, P., Wang, P., Zhou, J., Jiang, S., & Patel, V. M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00245 · Cited by 193 · see citing works

BEdgeHealth: A Decentralized Architecture for Edge-Based IoMT Networks Using Blockchain

Nguyen, D. C., Pathirana, P. N., Ding, M., & Seneviratne, A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3058953 · Cited by 184 · see citing works

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

Yan, R., Qu, L., Wei, Q., Huang, S.-C., Shen, L., Rubin, D. L., Xing, L., & Zhou, Y. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3233574 · Cited by 183 · see citing works

Decentralized Federated Learning for Healthcare Networks: A Case Study on Tumor Segmentation

Tedeschini, B. C., Savazzi, S., Stoklasa, R., Barbieri, L., Stathopoulos, I., Nicoli, M., & Serio, L. (2022) · IEEE Access · doi:10.1109/access.2022.3141913 · Cited by 175 · see citing works

HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images

Jiang, M., Wang, Z.-R., & Dou, Q. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19993 · Cited by 174 · see citing works

A multi-site, multi-disorder resting-state magnetic resonance image database

Tanaka, S., Yamashita, A., Yahata, N., Itahashi, T., Lisi, G., Yamada, T., Ichikawa, N., Takamura, M., Yoshihara, Y., Kunimatsu, A., et al. (2021) · Scientific Data · doi:10.1038/s41597-021-01004-8 · Cited by 169 · see citing works

Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard

Bulten, W., Bándi, P., Hoven, J., van de Loo, R., Lotz, J., Weiss, N., van der Laak, J. A. W. M., van Ginneken, B., de Kaa, C. H., & Litjens, G. J. S. (2019) · Scientific Reports · doi:10.1038/s41598-018-37257-4 · Cited by 161 · see citing works

InSiNet: a deep convolutional approach to skin cancer detection and segmentation

Reis, H. Ç., Turk, V., Khoshelham, K., & Kaya, S. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02473-0 · Cited by 161 · see citing works

FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection

Liu, Y., Fan, T., Chen, T., Xu, Q., & Yang, Q. (2021) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 156 · see citing works

Federated Learning and Blockchain-Enabled Fog-IoT Platform for Wearables in Predictive Healthcare

Baucas, M. J., Spachos, P., & Plataniotis, K. N. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3235950 · Cited by 154 · see citing works

On the Design of Blockchain-Based ECDSA With Fault-Tolerant Batch Verification Protocol for Blockchain-Enabled IoMT

Xiong, H., Jin, C., Alazab, M., Yeh, K., Wang, H., Gadekallu, T. R., Wang, W., & Su, C. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3112693 · Cited by 142 · see citing works

FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation

Wicaksana, J., Yan, Z., Zhang, D., Huang, X., Wu, H., Yang, X. Y., & Cheng, K.-T. T. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3233405 · Cited by 134 · see citing works

The General Data Protection Regulation in the Age of Surveillance Capitalism

Andrew, J., & Baker, M. (2019) · Journal of Business Ethics · doi:10.1007/s10551-019-04239-z · Cited by 131 · see citing works

Specificity-Preserving Federated Learning for MR Image Reconstruction

Feng, C.-M., Yan, Y., Wang, S., Xu, Y. D., Shao, L., & Fu, H. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3202106 · Cited by 131 · see citing works

Non-IID data and Continual Learning processes in Federated Learning: A long road ahead

Criado, M. F., Casado, F. E., Iglesias, R., Regueiro, C. V., & Barro, S. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.07.024 · Cited by 129 · see citing works

A systematic review of federated learning applications for biomedical data

Crowson, M. G., Moukheiber, D., Arévalo, A. R., Lam, B. D., Mantena, S., Rana, A., Goss, D., Bates, D. W., & Celi, L. A. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000033 · Cited by 129 · see citing works

FedNI: Federated Graph Learning With Network Inpainting for Population-Based Disease Prediction

Peng, L., Wang, N., Dvornek, N. C., Zhu, X., & Li, X. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3188728 · Cited by 128 · see citing works

A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison

Ek, S., Portet, F., Lalanda, P., & Vega, G. (2021) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom50583.2021.9439129 · Cited by 127 · see citing works

Personalized Real-Time Federated Learning for Epileptic Seizure Detection

Baghersalimi, S., Teijeiro, T., Atienza, D., & Aminifar, A. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3096127 · Cited by 117 · see citing works

Federated Learning of Generative Image Priors for MRI Reconstruction

Elmas, G., Dar, S. U. H., Korkmaz, Y., Ceyani, E., Susam, B., Özbey, M., Avestimehr, S., & Çukur, T. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3220757 · Cited by 117 · see citing works

Memory-aware curriculum federated learning for breast cancer classification

Jiménez-Sánchez, A., Tardy, M., González Ballester, M. A., Mateus, D., & Piella, G. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107318 · Cited by 114 · see citing works

Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder

Megerian, J. T., Dey, S., Melmed, R. D., Coury, D. L., Lerner, M., Nicholls, C. J., Sohl, K., Rouhbakhsh, R., Narasimhan, A., Romain, J. E., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00598-6 · Cited by 114 · see citing works

Backdoor Attacks and Defenses in Federated Learning: State-of-the-Art, Taxonomy, and Future Directions

Gong, X., Chen, Y., Wang, Q., & Kong, W. (2023) · IEEE Wireless Communications · doi:10.1109/mwc.017.2100714 · Cited by 114 · see citing works

Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

Chaddad, A., Lu, Q., Li, J., Katib, Y., Kateb, R., Tanougast, C., Bouridane, A., & Abdulkadir, A. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123123 · Cited by 112 · see citing works

Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning

Shi, J., Wan, W., Hu, S., Lu, J., & Zhang, L. Y. (2022) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom56396.2022.00030 · Cited by 108 · see citing works

BUSIS: A Benchmark for Breast Ultrasound Image Segmentation

Zhang, Y., Xian, M., Cheng, H.-D., Shareef, B., Ding, J., Xu, F., Huang, K., Zhang, B., Ning, C., & Wang, Y. (2022) · Healthcare · doi:10.3390/healthcare10040729 · Cited by 102 · see citing works

InFeMo: Flexible Big Data Management Through a Federated Cloud System

Stergiou, C. L., Psannis, K. E., & Gupta, B. B. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3426972 · Cited by 100 · see citing works

Federated disentangled representation learning for unsupervised brain anomaly detection

Bercea, C. I., Wiestler, B., Rueckert, D., & Albarqouni, S. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00515-2 · Cited by 99 · see citing works

DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction

Zhou, B., Dey, N., Schlemper, J., Salehi, S. S. M., Liu, C., Duncan, J. S., & Sofka, M. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00494 · Cited by 99 · see citing works

Personalized Retrogress-Resilient Federated Learning Toward Imbalanced Medical Data

Chen, Z., Yang, C., Zhu, M., Peng, Z., & Yuan, Y. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3192483 · Cited by 98 · see citing works

Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes

Draelos, R. L., Dov, D., Mazurowski, M. A., Lo, J. Y.-C., Henao, R., Rubin, G. D., & Carin, L. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101857 · Cited by 97 · see citing works

Curated Dataset for COVID-19 Posterior-Anterior Chest Radiography Images (X-Rays)

SAIT, U. (2020) · Data Archiving and Networked Services (DANS) · doi:10.17632/9xkhgts2s6 · Cited by 97 · see citing works

SplitAVG: A Heterogeneity-Aware Federated Deep Learning Method for Medical Imaging

Zhang, M., Qu, L., Singh, P., Kalpathy–Cramer, J., & Rubin, D. L. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3185956 · Cited by 96 · see citing works

Spine Explorer: a deep learning based fully automated program for efficient and reliable quantifications of the vertebrae and discs on sagittal lumbar spine MR images

Huang, J., Shen, H., Wu, J., Hu, X., Zhu, Z., Lv, X., Liu, Y., & Wang, Y. (2019) · The Spine Journal · doi:10.1016/j.spinee.2019.11.010 · Cited by 94 · see citing works

Personalized Federated Learning With Adaptive Batchnorm for Healthcare

Wang, L., Wang, J., Chen, Y., Qin, X., Xu, R., Dimitriadis, D., & Qin, T. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3177197 · Cited by 91 · see citing works

Contribution-Aware Federated Learning for Smart Healthcare

Liu, Z., Chen, Y., Zhao, Y., Yu, H., Liu, Y., Bao, R., Jiang, J., Nie, Z., Xu, Q., & Yang, Q. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21505 · Cited by 87 · see citing works

A Comprehensive Empirical Study of Heterogeneity in Federated Learning

Abdelmoniem, A. M., Ho, C.-Y., Papageorgiou, P., & Canini, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3250275 · Cited by 85 · see citing works

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

Kassem, H., Alapatt, D., Mascagni, P., Karargyris, A., & Padoy, N. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3222126 · Cited by 82 · see citing works

Deep neural network-based relation extraction: an overview

Wang, H., Qin, K., Zakari, R. Y., et al. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06667-3 · Cited by 79 · see citing works

Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation

Xu, A. P., Li, W., Guo, P., Yang, D., Roth, H. R., Hatamizadeh, A., Zhao, C., Xu, D., Huang, H., & Xu, Z. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.02020 · Cited by 77 · see citing works

An Automatic Method for Epileptic Seizure Detection Based on Deep Metric Learning

Duan, L., Wang, Z., Qiao, Y., Wang, Y., Huang, Z., & Zhang, B. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3138852 · Cited by 72 · see citing works

CD 2 -pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning

Shen, Y., Zhou, Y., & Yu, L. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00980 · Cited by 72 · see citing works

Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative

Pfaff, E., Girvin, A. T., Gabriel, D. L., Kostka, K., Morris, M. M., Palchuk, M. B., Lehmann, H. P., Amor, B., Bissell, M. M., Bradwell, K. R., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab217 · Cited by 71 · see citing works

Study of the performance and scalability of federated learning for medical imaging with intermittent clients

Díaz, J. S.-P., & García, Á. L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.011 · Cited by 70 · see citing works

Democratizing knowledge representation with BioCypher

Lobentanzer, S., Aloy, P., Baumbach, J., Bohár, B., Carey, V. J., Charoentong, P., Danhauser, K., Doğan, T., Dréo, J., Dunham, I., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01848-y · Cited by 68 · see citing works

Privacy-preserving and bandwidth-efficient federated learning: an application to in-hospital mortality prediction

Kerkouche, R., Ács, G., Castelluccia, C., & Genevès, P. (2021) · Proceedings of the Conference on Health, Inference, and Learning · doi:10.1145/3450439.3451859 · Cited by 65 · see citing works

Proportionally Fair Hospital Collaborations in Federated Learning of Histopathology Images

Hosseini, S. M., Sikaroudi, M., Babaie, M., & Tizhoosh, H. R. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3234450 · Cited by 59 · see citing works

Customized Federated Learning for Multi-Source Decentralized Medical Image Classification

Wicaksana, J., Yan, Z., Yang, X. Y., Liu, Y., Fan, L., & Cheng, K.-T. T. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3198440 · Cited by 56 · see citing works

Federated Transfer Learning-Based IDS for the Internet of Medical Things (IoMT)

Otoum, Y., Wan, Y., & Nayak, A. R. (2021) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps52748.2021.9682118 · Cited by 55 · see citing works

Exploring Segment-Level Semantics for Online Phase Recognition From Surgical Videos

Ding, X., & Li, X. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3182995 · Cited by 54 · see citing works

Towards Mitigating Device Heterogeneity in Federated Learning via Adaptive Model Quantization

Abdelmoniem, A. M., & Canini, M. (2021) · doi:10.1145/3437984.3458839 · Cited by 52 · see citing works

Federated Learning With Privacy-Preserving Ensemble Attention Distillation

Gong, X., Song, L., Vedula, R., Sharma, A., Zheng, M., Planche, B., Innanje, A., Chen, T., Yuan, J., Doermann, D., et al. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3213244 · Cited by 51 · see citing works

Brain Decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks

Li, H., & Fan, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00931-1_37 · Cited by 45 · see citing works

Heterogeneity-aware fair federated learning

Li, X., Zhao, S., Chen, C., & Zheng, Z. (2023) · Information Sciences · doi:10.1016/j.ins.2022.11.031 · Cited by 43 · see citing works

Federated Active Learning for Multicenter Collaborative Disease Diagnosis

Wu, X., Pei, J., Chen, C., Zhu, Y., Wang, J., Qian, Q., Zhang, J., Sun, Q., & Guo, Y. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3227563 · Cited by 42 · see citing works

Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI

Zhou, J., Zhou, L., Wang, D., Xu, X., Li, H., Chu, Y., Han, W., & Gao, X. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107861 · Cited by 42 · see citing works

Fedsld: Federated Learning with Shared Label Distribution for Medical Image Classification

Luo, J., & Wu, S. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761404 · Cited by 40 · see citing works

Weakly Supervised Liver Tumor Segmentation Using Couinaud Segment Annotation

Lyu, F., Ma, A. J., Yip, T. C., Wong, G. L., & Yuen, P. C. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3132905 · Cited by 40 · see citing works

Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image SegmentationPreprint

Xia, Y., Yang, D., Li, W., Myronenko, A., Xu, D., Obinata, H., Mori, H., An, P., Harmon, S. A., Turkbey, E. B., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.10195 · Cited by 39 · see citing works

A Silver Standard Corpus of Human Phenotype-Gene Relations

Sousa, D., Lamúrias, A., & Couto, F. M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1152 · Cited by 38 · see citing works

FedDM: Federated Weakly Supervised Segmentation via Annotation Calibration and Gradient De-Conflicting

Zhu, M., Chen, Z., & Yuan, Y. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3235757 · Cited by 36 · see citing works

Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking in Healthcare Applications

Thakur, A., Sharma, P., & Clifton, D. A. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3134835 · Cited by 33 · see citing works

FS-REAL: Towards Real-World Cross-Device Federated Learning

Chen, D., Gao, D., Xie, Y., Pan, X., Li, Z., Li, Y., Ding, B., & Zhou, J. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599829 · Cited by 32 · see citing works

Hyper-Graph Attention Based Federated Learning Methods for Use in Mental Health Detection

Ahmed, U., Lin, J. C., & Srivastava, G. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3172269 · Cited by 31 · see citing works

Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance

Jiang, M., Yang, H., Li, X., Liu, Q., Heng, P., & Dou, Q. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16437-8_19 · Cited by 31 · see citing works

Adaptation of IDPT System Based on Patient-Authored Text Data using NLP

Mukhiya, S. K., Ahmed, U., Rabbi, F., Pun, K. I., & Lamo, Y. (2020) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms49503.2020.00050 · Cited by 30 · see citing works

Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones

Zaridis, D. I., Mylona, E., Tachos, N. S., Pezoulas, V. C., Grigoriadis, G., Tsiknakis, N., Kostas Marias, A., Tsiknakis, M., & Fotiadis, D. I. (2023) · Scientific Reports · doi:10.1038/s41598-023-27671-8 · Cited by 28 · see citing works

Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones

Ek, S., Portet, F., Lalanda, P., et al. (2022) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2022.101714 · Cited by 27 · see citing works

DeepSDM: Boundary-aware pneumothorax segmentation in chest X-ray images

Wang, Y., Wang, K., Peng, X., Shi, L., Sun, J., Zheng, S., Shan, F., Shi, W., & Liu, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.029 · Cited by 26 · see citing works

Building ICU In-hospital Mortality Prediction Model with Federated Learning

Dang, T. K., Tan, K. C., Choo, M., Lim, N., Weng, J., & Feng, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63076-8_18 · Cited by 24 · see citing works

Federated learning-based vertebral body segmentation

Liu, J., Liang, X., Yang, R., et al. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105451 · Cited by 24 · see citing works

Diagnosing Autism Spectrum Disorder Using Ensemble 3D-CNN: A Preliminary Study

Deng, J., Hasan, M. R., Mahmud, M., et al. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897628 · Cited by 22 · see citing works

Multi-Task Distributed Learning Using Vision Transformer With Random Patch Permutation

Park, S. J., & Ye, J. C. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3218783 · Cited by 18 · see citing works

Pruning-Guided Curriculum Learning for Semi-Supervised Semantic Segmentation

Kong, H., Lee, G., Kim, S., et al. (2023) · IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv56688.2023.00586 · Cited by 18 · see citing works

A texture-based method for predicting molecular markers and survival outcome in lower grade glioma

Chaddad, A., Hassan, L., & Katib, Y. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04844-6 · Cited by 18 · see citing works

How Global Observation Works in Federated Learning: Integrating Vertical Training Into Horizontal Federated Learning

Wan, S., Lu, J., Fan, P., Shao, Y., Peng, C., Letaief, K. B., & Chuai, J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3234196 · Cited by 17 · see citing works

Federated Stain Normalization for Computational Pathology

Wagner, N., Fuchs, M., Tolkach, Y., & Mukhopadhyay, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_2 · Cited by 17 · see citing works

A Cross-Domain Federated Learning Framework for Wireless Human Sensing

Zhang, K., Liu, X., Xie, X. N., et al. (2022) · IEEE Network · doi:10.1109/mnet.001.2200231 · Cited by 17 · see citing works

FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver SegmentationPreprint

Bernecker, T., Peters, A., Schlett, C. L., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.11096 · Cited by 16 · see citing works

Direction and Residual Awareness Curriculum Learning Network for Rain Streaks Removal

Chang, Y., Chen, M., Yu, C., Li, Y., Chen, L., & Yan, L. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3227730 · Cited by 16 · see citing works

PolypGen: A multi-center polyp detection and segmentation dataset for generalisability assessment.Preprint

Ali, S., Jha, D., Ghatwary, N., Realdon, S., Cannizzaro, R., Salem, O. E., Lamarque, D., Daul, C., Ånonsen, K. V., Riegler, M. A., et al. (2021) · arXiv (Cornell University) · Cited by 9 · see citing works

A Federated Cox Model with Non-proportional Hazards

Zhang, D., Toni, F., & Williams, M. (2022) · Studies in computational intelligence · doi:10.1007/978-3-031-14771-5_12 · Cited by 9 · see citing works

Entropy-driven Fair and Effective Federated LearningPreprint

Wang, L.-C., Wang, Z., Shi, Y., Karimireddy, S. P., & Tang, X.-Y. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.12407 · Cited by 7 · see citing works

Deep Survival Analysis With Clinical Variables for COVID-19

Chaddad, A., Hassan, L., Katib, Y., & Bouridane, A. (2023) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2023.3256966 · Cited by 6 · see citing works

Federated Learning for COVID-19 Detection With Generative Adversarial Networks in Edge Cloud Computing

Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., & Zomaya, A. Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3120998 · Cited by 5 · see citing works

Federated attention consistent learning models for prostate cancer diagnosis and Gleason gradingPreprint

Kong, F., Wang, X., Xiang, J., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2302.06089 · Cited by 5 · see citing works

Local Environment Poisoning Attacks on Federated Reinforcement LearningPreprint

Ma, E. X., Rathi, P., & Etesami, S. R. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.02725 · Cited by 4 · see citing works

CNN Approach for Predicting Survival Outcome of Patients With COVID-19

Chaddad, A., & Tanougast, C. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3262882 · Cited by 4 · see citing works

Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical ImagingPreprint

Zheng, G., Jacobs, M. A., Braverman, V., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.06783 · Cited by 3 · see citing works

Federated Learning under Covariate Shifts with Generalization GuaranteesPreprint

Ramezani-Kebrya, A., Liu, F., Pethick, T., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.05325 · Cited by 3 · see citing works

Recurrrent Neural Network Based Model for Autism Spectrum Disorder Prediction using Codon Encoding

Sudha, V. P., & Vijaya, M. S. (2021) · Journal of The Institution of Engineers (India) Series B · doi:10.1007/s40031-021-00669-4 · Cited by 2 · see citing works

Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian NetworksPreprint

Liu, S., Guo, X., Qi, S., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.02423 · Cited by 2 · see citing works

Contrast with major classifier vectors for federated medical relation extraction with heterogeneous label distribution

Du, C., He, H., & Jin, Y. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05040-2 · Cited by 2 · see citing works

Lifespan Prediction for Lung and Bronchus Cancer Patients via Machine Learning Techniques

(2022) · International Journal of Machine Learning and Computing · doi:10.18178/ijmlc.2022.12.5.1107 · Cited by 1 · see citing works

Untitled work

(1999) · doi:10.1002/(sici)1097-0258(19990915/30)18:17/18

Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration

Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024) · Healthcare · doi:10.3390/healthcare12242587 · Cited by 228 · see citing works

ИСПОЛЬЗОВAНИЕ ПОТЕНЦИAЛA СОЦИAЛЬНЫХ ПAРТНЕРОВ В ПОДГОТОВКЕ БУДУЩИХ ПЕДAГОГОВ

Атабекова, Б. Б. (2024) · Habaršy. Pedagogika ġylymdary seriâsy · doi:10.51889/2959-5762.2023.80.4.019

DETERMINING QUALITY REQUIREMENTS AT THE UNIVERSITIES TO IMPROVE THE QUALITY OF EDUCATION

Mustafa, N. H., Kelesbayev, D., & Alibekova, Z. D. (2018) · The Journal of Economic Research & Business Administration · doi:10.26577/be-2018-2-2141

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Using DataGrid Control to Realize DataBase of Querying in VB6.0

Hu, S. (2000) · Modern Computer

ESKVS: efficient and secure approach for keyframes-based video summarization framework

Saini, P., & Berwal, K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18405-7 · Cited by 10 · see citing works