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Distributed Learning in Healthcare

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Trends of Artificial Intelligence and Big Data for E-Health

Part of the book series: Integrated Science ((IS,volume 9))

Abstract

Artificial intelligence and machine learning models are key tools in advancing data-driven healthcare solutions that aim to improve patient care and outcomes. A key step in developing accurate models is to train them on a large quantity and variety of data from several healthcare institutions. However, the standard approach of collecting data into a central database is often undesirable and not feasible in healthcare due to concerns regarding the privacy and security of patient data as well as more general data sharing restrictions. Distributed learning circumvents this need to share sensitive patient data by training models locally. This chapter reviews and discusses the principles and methods of distributed learning and describes its applications in the healthcare domain. Moreover, an overview of the current technical challenges and new advancements in distributed learning is provided, and important considerations in designing a distributed learning framework for healthcare applications discussed.

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References

  1. Vercio LL, Amador K, Bannister JJ, Crites S, Gutierrez A, MacDonald ME, Moore J, Mouches P, Rajashekar D, Schimert S, Subbanna N, Tuladhar A, Wang N, Wilms M, Winder A, Forkert ND (2020) Supervised machine learning tools: a tutorial for clinicians. J Neural Eng 17:062001. https://doi.org/10.1088/1741-2552/abbff2

    Article  Google Scholar 

  2. Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V (2019) Artificial intelligence transforms the future of healthcare. Am J Med 132:795–801. https://doi.org/10.1016/j.amjmed.2019.01.017

    Article  Google Scholar 

  3. Ginsburg GS, Phillips KA (2018) Precision medicine: from science to value. Health Affair 37:694–701. https://doi.org/10.1377/hlthaff.2017.1624

    Article  Google Scholar 

  4. MacEachern SJ, Forkert ND (2020) Machine learning for precision medicine. Genome. https://doi.org/10.1139/gen-2020-0131

    Article  Google Scholar 

  5. Pesapane F, Codari M, Sardanelli F (2018) Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Euro Radiol Exp 2:35. https://doi.org/10.1186/s41747-018-0061-6

    Article  Google Scholar 

  6. Tuladhar A, Schimert S, Rajashekar D, Kniep HC, Fiehler J, Forkert ND (2020) Automatic segmentation of stroke lesions in non-contrast computed tomography datasets with convolutional neural networks. IEEE Access 8:94871–94879. https://doi.org/10.1109/access.2020.2995632

    Article  Google Scholar 

  7. Halcox JPJ, Wareham K, Cardew A, Gilmore M, Barry JP, Phillips C, Gravenor MB (2017) Assessment of remote heart rhythm sampling using the AliveCor heart monitor to screen for atrial fibrillation. Circulation 136:1784–1794. https://doi.org/10.1161/circulationaha.117.030583

    Article  Google Scholar 

  8. Mouches P, Langner S, Domin M, Hill MD, Forkert ND (2021) Influence of cardiovascular risk-factors on morphological changes of cerebral arteries in healthy adults across the life span. Sci Rep-UK 11:12236. https://doi.org/10.1038/s41598-021-91669-3

    Article  CAS  Google Scholar 

  9. Phellan R, Lindner T, Helle M, Falcao A, Yasuda C, Sokolska M, Jager RH, Forkert ND (2019) Segmentation-based blood flow parameter refinement in cerebrovascular structures using 4D arterial spin labeling MRA. IEEE Trans Bio-Med Eng 67:1–1. https://doi.org/10.1109/tbme.2019.2951082

    Article  Google Scholar 

  10. Winder A, d’Esterre CD, Menon BK, Fiehler J, Forkert ND (2020) Automatic arterial input function selection in CT and MR perfusion datasets using deep convolutional neural networks. Med Phys 47:4199–4211. https://doi.org/10.1002/mp.14351

    Article  Google Scholar 

  11. Winder A, Wilms M, Fiehler J, Forkert ND (2021) Treatment efficacy analysis in acute ischemic stroke patients using in silico modeling based on machine learning: a proof-of-principle. Biomedicines 9:1357. https://doi.org/10.3390/biomedicines9101357

    Article  Google Scholar 

  12. Dorsey ER, Glidden AM, Holloway MR, Birbeck GL, Schwamm LH (2018) Teleneurology and mobile technologies: the future of neurological care. Nat Rev Neurol 14:285–297. https://doi.org/10.1038/nrneurol.2018.31

    Article  Google Scholar 

  13. Gill S, Mouches P, Hu S, Rajashekar D, MacMaster FP, Smith EE, Forkert ND, Ismail Z, Initiative ADN (2020) Using machine learning to predict dementia from neuropsychiatric symptom and neuroimaging data. J Alzheimer’s Dis 75:277–288. https://doi.org/10.3233/jad-191169

    Article  Google Scholar 

  14. Rajashekar D, Mouchès P, Fiehler J, Menon BK, Goyal M, Demchuk AM, Hill MD, Dukelow SP, Forkert ND (2020) Structural integrity of white matter tracts as a predictor of acute ischemic stroke outcome. Int J Stroke 15:965–972. https://doi.org/10.1177/1747493020915251

    Article  Google Scholar 

  15. Rajashekar D, Wilms M, Hecker KG, Hill MD, Dukelow S, Fiehler J, Forkert ND (2020) The impact of covariates in voxel-wise lesion-symptom mapping. Front Neurol 11:854. https://doi.org/10.3389/fneur.2020.00854

    Article  Google Scholar 

  16. Talai AS, Sedlacik J, Boelmans K, Forkert ND (2021) Utility of multi-modal MRI for differentiating of Parkinson’s disease and progressive supranuclear palsy using machine learning. Front Neurol 12:648548. https://doi.org/10.3389/fneur.2021.648548

    Article  Google Scholar 

  17. Lawton J, Blackburn M, Allen J, Campbell F, Elleri D, Leelarathna L, Rankin D, Tauschmann M, Thabit H, Hovorka R (2018) Patients’ and caregivers’ experiences of using continuous glucose monitoring to support diabetes self-management: qualitative study. BMC Endocr Disord 18:12. https://doi.org/10.1186/s12902-018-0239-1

    Article  CAS  Google Scholar 

  18. Niel O, Boussard C, Bastard P (2018) Artificial intelligence can predict GFR decline during the course of ADPKD. Am J Kidney Dis 71:911–912. https://doi.org/10.1053/j.ajkd.2018.01.051

    Article  Google Scholar 

  19. Livingstone D, Talai AS, Chau J, Forkert ND (2019) Building an otoscopic screening prototype tool using deep learning. J Otolaryngol- Head Neck Surg 48:66. https://doi.org/10.1186/s40463-019-0389-9

    Article  Google Scholar 

  20. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK (2021) Artificial intelligence in drug discovery and development. Drug Discov Today 26:80–93. https://doi.org/10.1016/j.drudis.2020.10.010

    Article  CAS  Google Scholar 

  21. Fleming N (2018) How artificial intelligence is changing drug discovery. Nature 557:S55–S57. https://doi.org/10.1038/d41586-018-05267-x

    Article  CAS  Google Scholar 

  22. Briganti G, Moine OL (2020) Artificial intelligence in medicine: today and tomorrow. Front Med 7:27. https://doi.org/10.3389/fmed.2020.00027

    Article  Google Scholar 

  23. Cuggia M, Combes S (2019) The French health data hub and the German medical informatics initiatives: two national projects to promote data sharing in healthcare. Yearbook Med Inform 28:195–202

    Article  Google Scholar 

  24. Charter for Safe Havens in Scotland, handling unconsented data from national health service patient records to support research and statistics. https://www.gov.scot/publications/charter-safe-havens-scotland-handling-unconsented-data-national-health-service-patient-records-support-research-statistics/pages/4/. Accessed 6 Dec 2021

  25. Health Data Research UK. https://www.hdruk.ac.uk/. Accessed 6 Dec 2021

  26. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Downey P, Elliott P, Green J, Landray M, Liu B, Matthews P, Ong G, Pell J, Silman A, Young A, Sprosen T, Peakman T, Collins R (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. Plos Med 12:e1001779. https://doi.org/10.1371/journal.pmed.1001779

    Article  Google Scholar 

  27. Canadian Health Measures Survey Biobank. https://www.statcan.gc.ca/en/microdata/biobank. Accessed 6 Dec 2021

  28. Demchuk AM, Goyal M, Menon BK, Eesa M, Ryckborst KJ, Kamal N, Patil S, Mishra S, Almekhlafi M, Randhawa PA, Roy D, Willinsky R, Montanera W, Silver FL, Shuaib A, Rempel J, Jovin T, Frei D, Sapkota B, Thornton JM, Poppe A, Tampieri D, Lum C, Weill A, Sajobi TT, Hill MD, Investigators ET (2014) Endovascular treatment for small core and anterior circulation proximal occlusion with emphasis on minimizing CT to recanalization times (ESCAPE) trial: methodology. Int J Stroke 10:429–438. https://doi.org/10.1111/ijs.12424

    Article  Google Scholar 

  29. Weiner MW, Aisen PS, Jack CR, Jagust WJ, Trojanowski JQ, Shaw L, Saykin AJ, Morris JC, Cairns N, Beckett LA, Toga A, Green R, Walter S, Soares H, Snyder P, Siemers E, Potter W, Cole PE, Schmidt M, Initiative ADN (2010) The Alzheimer’s disease neuroimaging initiative: progress report and future plans. Alzheimer’s Dementia 6:202-211.e7. https://doi.org/10.1016/j.jalz.2010.03.007

    Article  Google Scholar 

  30. Initiative TPPM, Kang J-H, Mollenhauer B, Coffey CS, Toledo JB, Weintraub D, Galasko DR, Irwin DJ, Deerlin VV, Chen-Plotkin AS, Caspell-Garcia C, Waligórska T, Taylor P, Shah N, Pan S, Zero P, Frasier M, Marek K, Kieburtz K, Jennings D, Tanner CM, Simuni T, Singleton A, Toga AW, Chowdhury S, Trojanowski JQ, Shaw LM (2016) CSF biomarkers associated with disease heterogeneity in early Parkinson’s disease: the Parkinson’s progression markers initiative study. Acta Neuropathol 131:935–949. https://doi.org/10.1007/s00401-016-1552-2

    Article  CAS  Google Scholar 

  31. Kang J-H, Irwin DJ, Chen-Plotkin AS, Siderowf A, Caspell C, Coffey CS, Waligórska T, Taylor P, Pan S, Frasier M, Marek K, Kieburtz K, Jennings D, Simuni T, Tanner CM, Singleton A, Toga AW, Chowdhury S, Mollenhauer B, Trojanowski JQ, Shaw LM, Lasch S, Flagg E, Poewe W, Sherer T, Meunier C, Rudolph A, Casaceli C, Seibyl J, Mendick S, Schuff N, Uribe L, Yankey J, Crawford K, Scutti A, Casalin P, Malferrari G, Hawkins K, Russell D, Leary L, Factor S, Sommerfeld B, Hogarth P, Pighetti E, Williams K, Standaert D, Guthrie S, Hauser R, Jankovic J, Hunter C, Stern M, Darin A, Leverenz J, Baca M, Frank S, Thomas C-A, Richard I, Deeley C, Rees L, Sprenger F, Oertel W, Willeke D, Shill H, Fernandez H, Mule J, Berg D, Gauss K, Galasko D, Fontaine D, Mari Z, McCoy A, Brooks D, Shah B, Barone P, Isaacson S, James A, Espay A, Espay K, Rowe D, Ranola M (2013) Association of cerebrospinal fluid β-amyloid 1–42, T-tau, P-tau181, and α-Synuclein levels with clinical features of drug-naive patients with early Parkinson disease. JAMA Neurol 70:1277–1287. https://doi.org/10.1001/jamaneurol.2013.3861

    Article  Google Scholar 

  32. LaMontagne PJ, Benzinger TLS, Morris JC, Keefe S, Hornbeck R, Xiong C, Grant E, Hassenstab J, Moulder K, Vlassenko AG, Raichle ME, Cruchaga C, Marcus D (2019) OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease. Medrxiv 2019.12.13.19014902. https://doi.org/10.1101/2019.12.13.19014902

  33. Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL (2007) Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J Cogn Neurosci 19:1498–1507. https://doi.org/10.1162/jocn.2007.19.9.1498

    Article  Google Scholar 

  34. Kaissis GA, Makowski MR, Rückert D, Braren RF (2020) Secure, privacy-preserving and federated machine learning in medical imaging. Nat Mach Intell 2:305–311. https://doi.org/10.1038/s42256-020-0186-1

    Article  Google Scholar 

  35. Rieke N, Hancox J, Li W, Milletarì F, Roth HR, Albarqouni S, Bakas S, Galtier MN, Landman BA, Maier-Hein K, Ourselin S, Sheller M, Summers RM, Trask A, Xu D, Baust M, Cardoso MJ (2020) The future of digital health with federated learning. NPJ Digital Med 3:119. https://doi.org/10.1038/s41746-020-00323-1

    Article  Google Scholar 

  36. Khan MA, Alkaabi N (2021) Rebirth of distributed AI—A review of eHealth research. Sensors 21:4999. https://doi.org/10.3390/s21154999

    Article  Google Scholar 

  37. Zerka F, Barakat S, Walsh S, Bogowicz M, Leijenaar RTH, Jochems A, Miraglio B, Townend D, Lambin P (2020) Systematic review of privacy-preserving distributed machine learning from federated databases in health care. JCO Clin Cancer Inform 4:184–200. https://doi.org/10.1200/cci.19.00047

    Article  Google Scholar 

  38. Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol 10:1–19. https://doi.org/10.1145/3298981

    Article  Google Scholar 

  39. Chang K, Balachandar N, Lam C, Yi D, Brown J, Beers A, Rosen B, Rubin DL, Kalpathy-Cramer J (2018) Distributed deep learning networks among institutions for medical imaging. J Am Med Inform Assoc 25:945–954. https://doi.org/10.1093/jamia/ocy017

    Article  Google Scholar 

  40. Dluhoš P, Schwarz D, Cahn W, van Haren N, Kahn R, Španiel F, Horáček J, Kašpárek T, Schnack H (2017) Multi-center machine learning in imaging psychiatry: a meta-model approach. Neuroimage 155:10–24. https://doi.org/10.1016/j.neuroimage.2017.03.027

    Article  Google Scholar 

  41. Tuladhar A, Gill S, Ismail Z, Forkert ND (2020) Building machine learning models without sharing patient data: a simulation-based analysis of distributed learning by ensembling. J Biomed Inform. https://doi.org/10.1016/j.jbi.2020.103424

    Article  Google Scholar 

  42. Konečný J, McMahan HB, Ramage D, Richtárik P (2016) Federated optimization: distributed machine learning for on-device intelligence. Arxiv

    Google Scholar 

  43. McMahan HB, Moore E, Ramage D, Hampson S, Arcas BA y (2016) Communication-efficient learning of deep networks from decentralized data. Arxiv

    Google Scholar 

  44. Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, Sarveswara R, Händler K, Pickkers P, Aziz NA, Ktena S, Tran F, Bitzer M, Ossowski S, Casadei N, Herr C, Petersheim D, Behrends U, Kern F, Fehlmann T, Schommers P, Lehmann C, Augustin M, Rybniker J, Altmüller J, Mishra N, Bernardes JP, Krämer B, Bonaguro L, Schulte-Schrepping J, Domenico ED, Siever C, Kraut M, Desai M, Monnet B, Saridaki M, Siegel CM, Drews A, Nuesch-Germano M, Theis H, Heyckendorf J, Schreiber S, Kim-Hellmuth S, (COVAS) C-19 AS, Balfanz P, Eggermann T, Boor P, Hausmann R, Kuhn H, Isfort S, Stingl JC, Schmalzing G, Kuhl CK, Röhrig R, Marx G, Uhlig S, Dahl E, Müller-Wieland D, Dreher M, Marx N, Nattermann J, Skowasch D, Kurth I, Keller A, Bals R, Nürnberg P, Rieß O, Rosenstiel P, Netea MG, Theis F, Mukherjee S, Backes M, Aschenbrenner AC, Ulas T, (DeCOI) DC-19 OI, Angelov A, Bartholomäus A, Becker A, Bezdan D, Blumert C, Bonifacio E, Bork P, Boyke B, Blum H, Clavel T, Colome-Tatche M, Cornberg M, Velázquez IADLR, Diefenbach A, Dilthey A, Fischer N, Förstner K, Franzenburg S, Frick J-S, Gabernet G, Gagneur J, Ganzenmueller T, Gauder M, Geißert J, Goesmann A, Göpel S, Grundhoff A, Grundmann H, Hain T, Hanses F, Hehr U, Heimbach A, Hoeper M, Horn F, Hübschmann D, Hummel M, Iftner T, Iftner A, Illig T, Janssen S, Kalinowski J, Kallies R, Kehr B, Keppler OT, Klein C, Knop M, Kohlbacher O, Köhrer K, Korbel J, Kremsner PG, Kühnert D, Landthaler M, Li Y, Ludwig KU, Makarewicz O, Marz M, McHardy AC, Mertes C, Münchhoff M, Nahnsen S, Nöthen M, Ntoumi F, Overmann J, Peter S, Pfeffer K, Pink I, Poetsch AR, Protzer U, Pühler A, Rajewsky N, Ralser M, Reiche K, Ripke S, Rocha UN da, Saliba A-E, Sander LE, Sawitzki B, Scheithauer S, Schiffer P, Schmid-Burgk J, Schneider W, Schulte E-C, Sczyrba A, Sharaf ML, Singh Y, Sonnabend M, Stegle O, Stoye J, Vehreschild J, Velavan TP, Vogel J, Volland S, Kleist M von, Walker A, Walter J, Wieczorek D, Winkler S, Ziebuhr J, Breteler MMB, Giamarellos-Bourboulis EJ, Kox M, Becker M, Cheran S, Woodacre MS, Goh EL, Schultze JL (2021) Swarm learning for decentralized and confidential clinical machine learning. Nature 594:265–270. https://doi.org/10.1038/s41586-021-03583-3

  45. Roy AG, Siddiqui S, Pölsterl S, Navab N, Wachinger C (2019) BrainTorrent: a peer-to-peer environment for decentralized federated learning. Arxiv

    Google Scholar 

  46. Jochems A, Deist TM, van Soest J, Eble M, Bulens P, Coucke P, Dries W, Lambin P, Dekker A (2016) Distributed learning: developing a predictive model based on data from multiple hospitals without data leaving the hospital—a real life proof of concept. Radiother Oncol 121:459–467. https://doi.org/10.1016/j.radonc.2016.10.002

    Article  Google Scholar 

  47. Liu B, Yan B, Zhou Y, Yang Y, Zhang Y (2020) Experiments of federated learning for COVID-19 chest x-ray images. Arxiv

    Google Scholar 

  48. Sheller MJ, Edwards B, Reina GA, Martin J, Pati S, Kotrotsou A, Milchenko M, Xu W, Marcus D, Colen RR, Bakas S (2020) Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep-UK 10:12598. https://doi.org/10.1038/s41598-020-69250-1

    Article  CAS  Google Scholar 

  49. Huang L, Shea AL, Qian H, Masurkar A, Deng H, Liu D (2019) Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. J Biomed Inform 99:103291. https://doi.org/10.1016/j.jbi.2019.103291

    Article  Google Scholar 

  50. Chen S, Xue D, Chuai G, Yang Q, Liu Q (2020) FL-QSAR: a federated learning-based QSAR prototype for collaborative drug discovery. Bioinformatics 36:5492–5498. https://doi.org/10.1093/bioinformatics/btaa1006

    Article  CAS  Google Scholar 

  51. Chen Y, Qin X, Wang J, Yu C, Gao W (2019) FedHealth: a federated transfer learning framework for wearable healthcare. IEEE Intell Syst 35:83–93. https://doi.org/10.1109/mis.2020.2988604

    Article  Google Scholar 

  52. Gudur GK, Perepu SK (2020) Federated learning with heterogeneous labels and models for mobile activity monitoring. Arxiv

    Google Scholar 

  53. Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018) Federated learning with non-IID data. Arxiv

    Google Scholar 

  54. Balachandar N, Chang K, Kalpathy-Cramer J, Rubin DL (2020) Accounting for data variability in multi-institutional distributed deep learning for medical imaging. J Am Med Inform Assoc 27:700–708. https://doi.org/10.1093/jamia/ocaa017

    Article  Google Scholar 

  55. Zerka F, Urovi V, Bottari F, Leijenaar RTH, Walsh S, Gabrani-Juma H, Gueuning M, Vaidyanathan A, Vos W, Occhipinti M, Woodruff HC, Dumontier M, Lambin P (2021) Privacy preserving distributed learning classifiers—sequential learning with small sets of data. Comput Biol Med 136:104716. https://doi.org/10.1016/j.compbiomed.2021.104716

    Article  Google Scholar 

  56. Beaulieu-Jones BK, Yuan W, Finlayson SG, Wu ZS (2018) Privacy-preserving distributed deep learning for clinical data. Arxiv

    Google Scholar 

  57. Qu L, Balachandar N, Rubin DL (2021) An experimental study of data heterogeneity in federated learning methods for medical imaging. Arxiv

    Google Scholar 

  58. French RM, French RM (1999) Catastrophic forgetting in connectionist networks. Trends Cogn Sci 3:128–135. https://doi.org/10.1016/s1364-6613(99)01294-2

    Article  CAS  Google Scholar 

  59. Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, Milan K, Quan J, Ramalho T, Grabska-Barwinska A, Hassabis D, Clopath C, Kumaran D, Hadsell R (2017) Overcoming catastrophic forgetting in neural networks. Proc Natl Acad Sci 114:3521–3526. https://doi.org/10.1073/pnas.1611835114

    Article  CAS  Google Scholar 

  60. Vepakomma P, Gupta O, Swedish T, Raskar R (2018) Split learning for health: distributed deep learning without sharing raw patient data. Arxiv

    Google Scholar 

  61. Gupta O, Raskar R (2018) Distributed learning of deep neural network over multiple agents. J Netw Comput Appl 116:1–8. https://doi.org/10.1016/j.jnca.2018.05.003

    Article  Google Scholar 

  62. Ha YJ, Yoo M, Lee G, Jung S, Choi SW, Kim J, Yoo S (2021) Spatio-temporal split learning for privacy-preserving medical platforms: case studies with COVID-19 CT, x-ray, and cholesterol data. IEEE Access 9:121046–121059. https://doi.org/10.1109/access.2021.3108455

    Article  Google Scholar 

  63. Poirot MG, Vepakomma P, Chang K, Kalpathy-Cramer J, Gupta R, Raskar R (2019) Split learning for collaborative deep learning in healthcare. Arxiv

    Google Scholar 

  64. Abuadbba S, Kim K, Kim M, Thapa C, Camtepe SA, Gao Y, Kim H, Nepal S (2020) Can we use split learning on 1D CNN models for privacy preserving training? Arxiv

    Google Scholar 

  65. Subbanna N, Wilms M, Tuladhar A, Forkert ND (2021) An analysis of the vulnerability of two common deep learning-based medical image segmentation techniques to model inversion attacks. Sensors 21:3874. https://doi.org/10.3390/s21113874

    Article  Google Scholar 

  66. Thapa C, Chamikara MAP, Camtepe S, Sun L (2020) SplitFed: when federated learning meets split learning. Arxiv

    Google Scholar 

  67. Yoo JH, Son HM, Jeong H, Jang E-H, Kim AY, Yu HY, Jeon HJ, Chung T-M (2021) Personalized federated learning with clustering: non-IID heart rate variability data application. Arxiv

    Google Scholar 

  68. Yeganeh Y, Farshad A, Navab N, Albarqouni S (2020) Inverse distance aggregation for federated learning with non-IID data. Arxiv

    Google Scholar 

  69. Wang H, Kaplan Z, Niu D, Li B (2020) Optimizing federated learning on non-IID data with reinforcement learning. In: IEEE Infocom 2020—IEEE Conference on Computer Communications, vol 00, pp 1698–1707. https://doi.org/10.1109/infocom41043.2020.9155494

  70. Huang Y, Chu L, Zhou Z, Wang L, Liu J, Pei J, Zhang Y (2020) Personalized cross-silo federated learning on non-IID data. Arxiv

    Google Scholar 

  71. Yang G, Mu K, Song C, Yang Z, Gong T (2021) RingFed: reducing communication costs in federated learning on non-IID data. Arxiv

    Google Scholar 

  72. Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, Bonawitz K, Charles Z, Cormode G, Cummings R, D’Oliveira RGL, Eichner H, Rouayheb SE, Evans D, Gardner J, Garrett Z, Gascón A, Ghazi B, Gibbons PB, Gruteser M, Harchaoui Z, He C, He L, Huo Z, Hutchinson B, Hsu J, Jaggi M, Javidi T, Joshi G, Khodak M, Konečný J, Korolova A, Koushanfar F, Koyejo S, Lepoint T, Liu Y, Mittal P, Mohri M, Nock R, Özgür A, Pagh R, Raykova M, Qi H, Ramage D, Raskar R, Song D, Song W, Stich SU, Sun Z, Suresh AT, Tramèr F, Vepakomma P, Wang J, Xiong L, Xu Z, Yang Q, Yu FX, Yu H, Zhao S (2019) Advances and open problems in federated learning. Arxiv

    Google Scholar 

  73. Tan AZ, Yu H, Cui L, Yang Q (2021) Towards personalized federated learning. Arxiv

    Google Scholar 

  74. Smith V, Chiang C-K, Sanjabi M, Talwalkar A (2017) Federated Multi-Task Learning. Arxiv

    Google Scholar 

  75. Arivazhagan MG, Aggarwal V, Singh AK, Choudhary S (2019) Federated learning with personalization layers. Arxiv

    Google Scholar 

  76. Korngiebel DM, Mooney SD (2021) Considering the possibilities and pitfalls of generative pre-trained transformer 3 (GPT-3) in healthcare delivery. NPJ Digital Med 4:93. https://doi.org/10.1038/s41746-021-00464-x

    Article  Google Scholar 

  77. Li Y, Rao S, Solares JRA, Hassaine A, Ramakrishnan R, Canoy D, Zhu Y, Rahimi K, Salimi-Khorshidi G (2020) BEHRT: transformer for electronic health records. Sci Rep-UK 10:7155. https://doi.org/10.1038/s41598-020-62922-y

    Article  CAS  Google Scholar 

  78. Caldas S, Konečny J, McMahan HB, Talwalkar A (2018) Expanding the reach of federated learning by reducing client resource requirements. Arxiv

    Google Scholar 

  79. Zhou Y, Pu G, Ma X, Li X, Wu D (2020) Distilled one-shot federated learning. Arxiv

    Google Scholar 

  80. Wang J, Hu J, Mills J, Min G (2021) Federated ensemble model-based reinforcement learning. Arxiv

    Google Scholar 

  81. Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. Arxiv

    Google Scholar 

  82. Jiang Y, Wang S, Valls V, Ko BJ, Lee W-H, Leung KK, Tassiulas L (2019) Model pruning enables efficient federated learning on edge devices. Arxiv

    Google Scholar 

  83. Zhu M, Gupta S (2017) To prune, or not to prune: exploring the efficacy of pruning for model compression. Arxiv

    Google Scholar 

  84. Konečný J, McMahan HB, Yu FX, Richtárik P, Suresh AT, Bacon D (2016) Federated learning: strategies for improving communication efficiency. Arxiv

    Google Scholar 

  85. Bouacida N, Hou J, Zang H, Liu X (2020) Adaptive federated dropout: improving communication efficiency and generalization for federated learning. Arxiv

    Google Scholar 

  86. Kamp M, Adilova L, Sicking J, Hüger F, Schlicht P, Wirtz T, Wrobel S (2018) Efficient decentralized deep learning by dynamic model averaging. Arxiv

    Google Scholar 

  87. Bui TD, Nguyen CV, Swaroop S, Turner RE (2018) Partitioned variational inference: a unified framework encompassing federated and continual learning. Arxiv

    Google Scholar 

  88. Guha N, Talwalkar A, Smith V (2019) One-shot federated learning. Arxiv

    Google Scholar 

  89. Sharifnassab A, Salehkaleybar S, Golestani SJ (2019) Order optimal one-shot distributed learning. Arxiv

    Google Scholar 

  90. Cho YJ, Wang J, Joshi G (2020) Client selection in federated learning: convergence analysis and power-of-choice selection strategies. Arxiv

    Google Scholar 

  91. Shokri R, Shmatikov V (2015) Privacy-preserving deep learning. In: Proceedings of 22nd ACM SIGSAC conference on computer and communications security, pp 1310–1321. https://doi.org/10.1145/2810103.2813687

  92. Yu S, Chen X, Zhou Z, Gong X, Wu D (2020) When deep reinforcement learning meets federated learning: intelligent multi-timescale resource management for multi-access edge computing in 5G ultra dense network. Arxiv

    Google Scholar 

  93. Anh TT, Luong NC, Niyato D, Kim DI, Wang L-C (2019) Efficient training management for mobile crowd-machine learning: a deep reinforcement learning approach. IEEE Wirel Commun Lett 8:1345–1348. https://doi.org/10.1109/lwc.2019.2917133

    Article  Google Scholar 

  94. Lyu L, Yu H, Yang Q (2020) Threats to federated learning: a survey. Arxiv

    Google Scholar 

  95. Hernández-Cano A, Cammarota R, Imani M (2021) PRID: model inversion privacy attacks in hyperdimensional learning systems. In: 2021 58th ACM IEEE design automation Conference, DAC, vol 00, pp 553–558. https://doi.org/10.1109/dac18074.2021.9586217

  96. Zhang Y, Jia R, Pei H, Wang W, Li B, Song D (2020) The secret revealer: generative model-inversion attacks against deep neural networks. In: 2020 IEEE CVF conference on computer vision and pattern recognition, CVPR, vol 00, pp 250–258. https://doi.org/10.1109/cvpr42600.2020.00033

  97. Nasr M, Shokri R, Houmansadr A (2019) Comprehensive privacy analysis of deep learning: passive and active white-box inference attacks against centralized and federated learning, pp 739–753

    Google Scholar 

  98. Wu B, Zhao S, Sun G, Zhang X, Su Z, Zeng C, Liu Z (2019) P3SGD: patient privacy preserving SGD for regularizing deep CNNs in pathological image classification. In: 2019 IEEE CVF conference on computer vision and pattern recognition, CVPR, vol 00, pp 2094–2103. https://doi.org/10.1109/cvpr.2019.00220

  99. Wang Z, Song M, Zhang Z, Song Y, Wang Q, Qi H (2019) Beyond inferring class representatives: user-level privacy leakage from federated learning. In: IEEE Infocom 2019—IEEE conference on computer communication, vol 00, pp 2512–2520. https://doi.org/10.1109/infocom.2019.8737416

  100. Ligeng Z, Song H (2020) Deep leakage from gradients. Springer International Publishing, Cham, pp 17–31

    Google Scholar 

  101. Thuraisingham B, Evans D, Malkin T, Xu D, Hitaj B, Ateniese G, Perez-Cruz F (2017) Deep models under the GAN. In: Proceedings of the 2017 ACM SIGSAC conference on computing and communications security, pp 603–618. https://doi.org/10.1145/3133956.3134012

  102. Christen P (2012) Data matching, concepts and techniques for record linkage, entity resolution, and duplicate detection. https://doi.org/10.1007/978-3-642-31164-2

  103. Xu J, Xu Z, Walker P, Wang F (2020) Federated patient hashing. Proc AAAI Conf Artif Intell 34:6486–6493. https://doi.org/10.1609/aaai.v34i04.6121

    Article  Google Scholar 

  104. Li W, Milletarì F, Xu D, Rieke N, Hancox J, Zhu W, Baust M, Cheng Y, Ourselin S, Cardoso MJ, Feng A (2019) Privacy-preserving federated brain tumour segmentation. Arxiv

    Google Scholar 

  105. Lyu L (2021) DP-SIGNSGD: when efficiency meets privacy and robustness. In: ICASSP 2021—2021 IEEE international conference on acoustics, speech, and signal processing, ICASSP, vol 00, pp 3070–3074. https://doi.org/10.1109/icassp39728.2021.9414538

  106. Wei K, Li J, Ding M, Ma C, Yang HH, Farokhi F, Jin S, Quek TQS, Poor HV (2020) Federated learning with differential privacy: algorithms and performance analysis. IEEE Trans Inform Foren Sec 15:3454–3469. https://doi.org/10.1109/tifs.2020.2988575

    Article  Google Scholar 

  107. Ziller A, Usynin D, Remerscheid N, Knolle M, Makowski M, Braren R, Rueckert D, Kaissis G (2021) Differentially private federated deep learning for multi-site medical image segmentation. Arxiv

    Google Scholar 

  108. Malekzadeh M, Hasircioglu B, Mital N, Katarya K, Ozfatura ME, Gündüz D (2021) Dopamine: differentially private federated learning on medical data. Arxiv

    Google Scholar 

  109. Taihong X, Yi-Hsuan T, Kihyuk S, Manmohan C, Ming-Hsuan Y (2020) Adversarial learning of privacy-preserving and task-oriented representations. Proc AAAI Conf Artif Intell 34:12434–12441. https://doi.org/10.1609/aaai.v34i07.6930

    Article  Google Scholar 

  110. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial networks. arXiv.org

    Google Scholar 

  111. Croft WL, Sack J-R, Shi W (2021) Obfuscation of images via differential privacy: from facial images to general images. Peer Peer Netw Appl 14:1705–1733. https://doi.org/10.1007/s12083-021-01091-9

    Article  Google Scholar 

  112. Liyue F (2018) Image pixelization with differential privacy. Springer International Publishing, pp 148–162

    Google Scholar 

  113. Li T, Sahu AK, Talwalkar A, Smith V (2020) Federated learning: challenges, methods, and future directions. IEEE Sign Proc Maga 37:50–60. https://doi.org/10.1109/msp.2020.2975749

    Article  CAS  Google Scholar 

  114. Fang M, Cao X, Jia J, Gong NZ (2019) Local model poisoning attacks to byzantine-robust federated learning. Arxiv

    Google Scholar 

  115. Sun G, Cong Y, Dong J, Wang Q, Lyu L, Liu J (2021) Data poisoning attacks on federated machine learning. IEEE Internet Things, p 1. https://doi.org/10.1109/jiot.2021.3128646

  116. Zhao C, Zhao S, Zhao M, Chen Z, Gao C-Z, Li H, Tan Y (2019) Secure multi-party computation: theory, practice and applications. Inform Sci 476:357–372. https://doi.org/10.1016/j.ins.2018.10.024

    Article  Google Scholar 

  117. Acar A, Aksu H, Uluagac AS, Conti M (2018) A survey on homomorphic encryption schemes: theory and implementation. ACM Comput Surv Csur 51:79. https://doi.org/10.1145/3214303

    Article  Google Scholar 

  118. Hesamifard E, Takabi H, Ghasemi M (2017) CryptoDL: deep neural networks over encrypted data. Arxiv

    Google Scholar 

  119. Simonyan K, Vedaldi A, Zisserman A (2013) Deep inside convolutional networks: visualising image classification models and saliency maps. Arxiv

    Google Scholar 

  120. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International conference on computer vision, ICCV, pp 618–626. https://doi.org/10.1109/iccv.2017.74

  121. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł ukasz, Polosukhin I, Attention is all you need. In: Advances in neural information processing systems. Curran Associates, Inc.

    Google Scholar 

  122. Byrne RMJ (2019) Counterfactuals in explainable artificial intelligence (XAI): evidence from human reasoning. In: Proceedings of the twenty-eighth international joint conference on artificial intelligence, IJCAI-19. International joint conferences on artificial intelligence organization, pp 6276–6282

    Google Scholar 

  123. Kobyzev I, Prince SJD, Brubaker MA (2019) Normalizing flows: an introduction and review of current methods. IEEE Trans Pattern Anal 43:3964–3979. https://doi.org/10.1109/tpami.2020.2992934

    Article  Google Scholar 

  124. Wilms M, Mouches P, Bannister JJ, Rajashekar D, Langer S, Forkert NDD, Nils F (2021) Towards self-explainable classifiers and regressors in neuroimaging with normalizing flows. In: Abdulkadir A et al (eds) Machine learning in clinical neuroimaging. MLCN 2021. Lecture notes in computer science, vol 13001. Springer, Cham

    Google Scholar 

  125. Wang G (2019) Interpret federated learning with shapley values. Arxiv

    Google Scholar 

  126. Gupta R, Liu Y, Tang J, Prakash BA, Ahmad MA, Patel A, Eckert C, Kumar V, Teredesai A (2020) Fairness in machine learning for healthcare. In: Proc 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 3529–3530. https://doi.org/10.1145/3394486.3406461

  127. Bahadori MT, Heckerman DE (2020) Debiasing concept-based explanations with causal analysis. Arxiv

    Google Scholar 

  128. Chen Z, Bei Y, Rudin C (2020) Concept whitening for interpretable image recognition. Nat Mach Intell 2:772–782. https://doi.org/10.1038/s42256-020-00265-z

    Article  Google Scholar 

  129. Acar DAE, Zhao Y, Zhu R, Matas R, Mattina M, Whatmough P, Saligrama V (2021) Debiasing model updates for improving personalized federated training 139:21–31

    Google Scholar 

  130. Barbano CA, Tartaglione E, Grangetto M (2021) Bridging the gap between debiasing and privacy for deep learning. In: 2021 IEEE CVF International conference on computer vision and working, ICCVW, vol 00, pp 3799–3808. https://doi.org/10.1109/iccvw54120.2021.00424

  131. Glocker B, Robinson R, Castro DC, Dou Q, Konukoglu E (2019) Machine learning with multi-site imaging data: an empirical study on the impact of scanner effects. Arxiv

    Google Scholar 

  132. Robert R, Qi D, de Daniel C, Konstantinos K, de Marius G, Ronald MS, Daniel R, Ben G (2020) Image-level harmonization of multi-site data using image-and-spatial transformer networks. Springer International Publishing, pp 710–719

    Google Scholar 

  133. Bashyam VM, Doshi J, Erus G, Srinivasan D, Abdulkadir A, Habes M, Fan Y, Masters CL, Maruff P, Zhuo C, Völzke H, Johnson SC, Fripp J, Koutsouleris N, Satterthwaite TD, Wolf DH, Gur RE, Gur RC, Morris JC, Albert MS, Grabe HJ, Resnick SM, Bryan RN, Wolk DA, Shou H, Nasrallah IM, Davatzikos C (2020) Medical image harmonization using deep learning based canonical mapping: toward robust and generalizable learning in imaging. Arxiv

    Google Scholar 

  134. Modanwal G, Vellal A, Buda M, Mazurowski MA (2020) MRI image harmonization using cycle-consistent generative adversarial network. In: Medical imaging 2020 comput diagnosis, vol 11314, pp 1131413-1131413–6. https://doi.org/10.1117/12.2551301

  135. Bourtoule L, Chandrasekaran V, Choquette-Choo CA, Jia H, Travers A, Zhang B, Lie D, Papernot N (2021) Machine unlearning. In: 2021 IEEE symposium on security and privacy, vol 00, pp 141–159.https://doi.org/10.1109/sp40001.2021.00019

  136. Villaronga EF, Kieseberg P, Li T (2018) Humans forget, machines remember: artificial intelligence and the right to be forgotten. Comput Law Secur Rev 34:304–313. https://doi.org/10.1016/j.clsr.2017.08.007

    Article  Google Scholar 

  137. Nelson K, Corbin G, Anania M, Kovacs M, Tobias J, Blowers M (2015) Evaluating model drift in machine learning algorithms. In: 2015 IEEE symposium on computational intelligence for security and defence applications, CISDA, pp 1–8.https://doi.org/10.1109/cisda.2015.7208643

  138. Tianhao W, Johannes R, Ce Z, Ruoxi J, Dawn S (2020) A principled approach to data valuation for federated learning. Springer International Publishing, Cham, pp 153–167

    Google Scholar 

  139. Shuyue W, Yongxin T, Zimu Z, Tianshu S (2020) Efficient and fair data valuation for horizontal federated learning. Springer International Publishing, Cham, pp 139–152

    Google Scholar 

  140. Fan Z, Fang H, Zhou Z, Pei J, Friedlander MP, Liu C, Zhang Y (2021) Improving fairness for data valuation in federated learning. Arxiv

    Google Scholar 

  141. Detrano R, Janosi A, Steinbrunn W, Pfisterer M, Schmid J-J, Sandhu S, Guppy KH, Lee S, Froelicher V (1989) International application of a new probability algorithm for the diagnosis of coronary artery disease. Am J Cardiol 64:304–310. https://doi.org/10.1016/0002-9149(89)90524-9

    Article  CAS  Google Scholar 

  142. Smith JW, Everhart JE, Dickson WC, Knowler WC, Johannes RS (1988) Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In: Proceedings of the annual symposium on computer application in medical care, pp 261–265

    Google Scholar 

  143. Wolberg WH, Street WN, Heisey DM, Mangasarian OL (1995) Computer-derived nuclear features distinguish malignant from benign breast cytology. Hum Pathol 26:792–796. https://doi.org/10.1016/0046-8177(95)90229-5

    Article  CAS  Google Scholar 

  144. Kaggle Diabetic Retinopathy Detection. https://www.kaggle.com/c/diabetic-retinopathy-detection/overview. Accessed 6 Dec 2021

  145. USF Digital Mammography. http://www.eng.usf.edu/cvprg/mammography/database.html. Accessed 6 Dec 2021

  146. Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O (2018) The eICU collaborative research database, a freely available multi-center database for critical care research. Sci Data 5:180178. https://doi.org/10.1038/sdata.2018.178

    Article  Google Scholar 

  147. Brisimi TS, Chen R, Mela T, Olshevsky A, Paschalidis ICh, Shi W (2018) Federated learning of predictive models from federated electronic health records. Int J Med Inform 112:59–67. https://doi.org/10.1016/j.ijmedinf.2018.01.007

  148. Choudhury O, Park Y, Salonidis T, Gkoulalas-Divanis A, Sylla I, Das AK (2019) Predicting adverse drug reactions on distributed health data using federated learning. In: AMIA annual symposium proceedings, AMIA symposium, vol 2019, pp 313–322

    Google Scholar 

  149. Song J, Abdelzaher T, Mascolo C, Stisen A, Blunck H, Bhattacharya S, Prentow TS, Kjærgaard MB, Dey A, Sonne T, Jensen MM (2015) Smart devices are different. In: Proceedings of the 13th ACM conference on embedded networked sensor systems, pp 127–140. https://doi.org/10.1145/2809695.2809718

  150. Wu Q, Chen X, Zhou Z, Zhang J (2020) FedHome: cloud-edge based personalized federated learning for in-home health monitoring. IEEE Trans Mobile Comput, p 1. https://doi.org/10.1109/tmc.2020.3045266

  151. George V, Charikleia C, Thodoris M, Matthew P, Manolis T, The mobiact dataset: recognition of activities of daily living using smartphones. SCITEPRESS, pp 143–151

    Google Scholar 

  152. Davide A, Alessandro G, Luca O, Xavier P, Jorge LR-O (2012) Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. Springer, Berlin Heidelberg, pp 216–223

    Google Scholar 

  153. Wang L, Lin ZQ, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep-UK 10:19549. https://doi.org/10.1038/s41598-020-76550-z

    Article  CAS  Google Scholar 

  154. Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber M-A, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp Ç, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SMS, Ryan M, Sarikaya D, Schwartz L, Shin H-C, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Leemput KV (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34:1993–2024. https://doi.org/10.1109/tmi.2014.2377694

    Article  Google Scholar 

  155. Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, Marklund H, Haghgoo B, Ball R, Shpanskaya K, Seekins J, Mong DA, Halabi SS, Sandberg JK, Jones R, Larson DB, Langlotz CP, Patel BN, Lungren MP, Ng AY (2019) CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. Arxiv

    Google Scholar 

  156. Moody GB, Mark RG (2001) The impact of the MIT-BIH Arrhythmia database. IEEE Eng Med Biol 20:45–50. https://doi.org/10.1109/51.932724

    Article  CAS  Google Scholar 

  157. Rajpurkar P, Irvin J, Bagul A, Ding D, Duan T, Mehta H, Yang B, Zhu K, Laird D, Ball RL, Langlotz C, Shpanskaya K, Lungren MP, Ng AY (2017) MURA: large dataset for abnormality detection in musculoskeletal radiographs. Arxiv

    Google Scholar 

  158. Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM (2017) ChestX-Ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: 2017 IEEE conference on computer vision and pattern Recognition, CVPR, pp 3462–3471. https://doi.org/10.1109/cvpr.2017.369

  159. Bakr S, Gevaert O, Echegaray S, Ayers K, Zhou M, Shafiq M, Zheng H, Benson JA, Zhang W, Leung ANC, Kadoch M, Hoang CD, Shrager J, Quon A, Rubin DL, Plevritis SK, Napel S (2018) A radiogenomic dataset of non-small cell lung cancer. Sci Data 5:180202. https://doi.org/10.1038/sdata.2018.202

    Article  CAS  Google Scholar 

  160. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26:1045–1057. https://doi.org/10.1007/s10278-013-9622-7

    Article  Google Scholar 

  161. Oberije C, Ruysscher DD, Houben R, van de Heuvel M, Uyterlinde W, Deasy JO, Belderbos J, Dingemans A-MC, Rimner A, Din S, Lambin P (2015) A validated prediction model for overall survival from stage iii non-small cell lung cancer: toward survival prediction for individual patients. Int J Radiat Oncol Biology Phys 92:935–944. https://doi.org/10.1016/j.ijrobp.2015.02.048

    Article  Google Scholar 

  162. Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM, Network CGAR (2013) The cancer genome atlas pan-cancer analysis project. Nat Genet 45:1113–1120. https://doi.org/10.1038/ng.2764

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Acknowledgements

This work was supported by the Natural Sciences and Engineering Research Council of Canada (AT, NDF), Canadian Institutes of Health Research (NDF), Canada Research Chairs program (NDF), and the River Fund at Calgary Foundation (NDF).

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Authors and Affiliations

  1. Department of Radiology, University of Calgary, Calgary, AB, Canada

    Anup Tuladhar, Deepthi Rajashekar & Nils D. Forkert

  2. Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada

    Anup Tuladhar, Deepthi Rajashekar & Nils D. Forkert

  3. Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada

    Anup Tuladhar, Deepthi Rajashekar & Nils D. Forkert

  4. Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada

    Nils D. Forkert

  5. Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada

    Nils D. Forkert

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  1. Anup Tuladhar
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Correspondence to Anup Tuladhar .

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Editors and Affiliations

  1. National School of Computer Science (ENSI), Manouba University, COSMOS Laboratory, Manouba, Tunisia

    Houneida Sakly

  2. Department of Radiology, Stanford University, Palo Alto, CA, USA

    Kristen Yeom

  3. Northwestern University, Chicago, IL, USA

    Safwan Halabi

  4. Radiologist and Associate Professor in medical Imaging, Carthage Medical International Center Monastir and Sousse Medina Medical Imaging Center, Monastir, Tunisia

    Mourad Said

  5. Department Radiology, Stanford Medicine, Palo Alto, CA, USA

    Jayne Seekins

  6. National School of Computer Science (ENSI), Manouba University, COSMOS Laboratory, Manouba, Tunisia

    Moncef Tagina

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Tuladhar, A., Rajashekar, D., Forkert, N.D. (2022). Distributed Learning in Healthcare. In: Sakly, H., Yeom, K., Halabi, S., Said, M., Seekins, J., Tagina, M. (eds) Trends of Artificial Intelligence and Big Data for E-Health. Integrated Science, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-031-11199-0_10

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