Federated learning enables big data for rare cancer boundary detection.

Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S

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DOI
10.1038/s41467-022-33407-5
Published
2022 Dec 5
Container
Nature communications
Publisher
Not recorded
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yes

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BibTeX

@article{allodium:10.1038/s41467-022-33407-5,
  title = {Federated learning enables big data for rare cancer boundary detection.},
  author = {Pati S and Baid U and Edwards B and Sheller M and Wang SH and Reina GA and Foley P and Gruzdev A and Karkada D and Davatzikos C and Sako C and Ghodasara S and Bilello M and Mohan S and Vollmuth P and Brugnara G and Preetha CJ and Sahm F and Maier-Hein K and Zenk M and Bendszus M and Wick W and Calabrese E and Rudie J and Villanueva-Meyer J and Cha S and Ingalhalikar M and Jadhav M and Pandey U and Saini J and Garrett J and Larson M and Jeraj R and Currie S and Frood R and Fatania K and Huang RY and Chang K and Balaña C and Capellades J and Puig J and Trenkler J and Pichler J and Necker G and Haunschmidt A and Meckel S and Shukla G and Liem S and Alexander GS and Lombardo J and Palmer JD and Flanders AE and Dicker AP and Sair HI and Jones CK and Venkataraman A and Jiang M and So TY and Chen C and Heng PA and Dou Q and Kozubek M and Lux F and Michálek J and Matula P and Keřkovský M and Kopřivová T and Dostál M and Vybíhal V and Vogelbaum MA and Mitchell JR and Farinhas J and Maldjian JA and Yogananda CGB and Pinho MC and Reddy D and Holcomb J and Wagner BC and Ellingson BM and Cloughesy TF and Raymond C and Oughourlian T and Hagiwara A and Wang C and To MS and Bhardwaj S and Chong C and Agzarian M and Falcão AX and Martins SB and Teixeira BCA and Sprenger F and Menotti D and Lucio DR and LaMontagne P and Marcus D and Wiestler B and Kofler F and Ezhov I and Metz M and Jain R and Lee M and Lui YW and McKinley R and Slotboom J and Radojewski P and Meier R and Wiest R and Murcia D and Fu E and Haas R and Thompson J and Ormond DR and Badve C and Sloan AE and Vadmal V and Waite K and Colen RR and Pei L and Ak M and Srinivasan A and Bapuraj JR and Rao A and Wang N and Yoshiaki O and Moritani T and Turk S and Lee J and Prabhudesai S and Morón F and Mandel J and Kamnitsas K and Glocker B and Dixon LVM and Williams M and Zampakis P and Panagiotopoulos V and Tsiganos P and Alexiou S and Haliassos I and Zacharaki EI and Moustakas K and Kalogeropoulou C and Kardamakis DM and Choi YS and Lee SK and Chang JH and Ahn SS and Luo B and Poisson L and Wen N and Tiwari P and Verma R and Bareja R and Yadav I and Chen J and Kumar N and Smits M and van der Voort SR and Alafandi A and Incekara F and Wijnenga MMJ and Kapsas G and Gahrmann R and Schouten JW and Dubbink HJ and Vincent AJPE and van den Bent MJ and French PJ and Klein S and Yuan Y and Sharma S and Tseng TC and Adabi S and Niclou SP and Keunen O and Hau AC and Vallières M and Fortin D and Lepage M and Landman B and Ramadass K and Xu K and Chotai S and Chambless LB and Mistry A and Thompson RC and Gusev Y and Bhuvaneshwar K and Sayah A and Bencheqroun C and Belouali A and Madhavan S and Booth TC and Chelliah A and Modat M and Shuaib H and Dragos C and Abayazeed A and Kolodziej K and Hill M and Abbassy A and Gamal S and Mekhaimar M and Qayati M and Reyes M and Park JE and Yun J and Kim HS and Mahajan A and Muzi M and Benson S and Beets-Tan RGH and Teuwen J and Herrera-Trujillo A and Trujillo M and Escobar W and Abello A and Bernal J and Gómez J and Choi J and Baek S and Kim Y and Ismael H and Allen B and Buatti JM and Kotrotsou A and Li H and Weiss T and Weller M and Bink A and Pouymayou B and Shaykh HF and Saltz J and Prasanna P and Shrestha S and Mani KM and Payne D and Kurc T and Pelaez E and Franco-Maldonado H and Loayza F and Quevedo S and Guevara P and Torche E and Mendoza C and Vera F and Ríos E and López E and Velastin SA and Ogbole G and Soneye M and Oyekunle D and Odafe-Oyibotha O and Osobu B and Shu'aibu M and Dorcas A and Dako F and Simpson AL and Hamghalam M and Peoples JJ and Hu R and Tran A and Cutler D and Moraes FY and Boss MA and Gimpel J and Veettil DK and Schmidt K and Bialecki B and Marella S and Price C and Cimino L and Apgar C and Shah P and Menze B and Barnholtz-Sloan JS and Martin J and Bakas S},
  year = {2022},
  journal = {Nature communications},
  doi = {10.1038/s41467-022-33407-5},
  url = {https://doi.org/10.1038/s41467-022-33407-5}
}

RIS

TY  - JOUR
TI  - Federated learning enables big data for rare cancer boundary detection.
AU  - Pati S
AU  - Baid U
AU  - Edwards B
AU  - Sheller M
AU  - Wang SH
AU  - Reina GA
AU  - Foley P
AU  - Gruzdev A
AU  - Karkada D
AU  - Davatzikos C
AU  - Sako C
AU  - Ghodasara S
AU  - Bilello M
AU  - Mohan S
AU  - Vollmuth P
AU  - Brugnara G
AU  - Preetha CJ
AU  - Sahm F
AU  - Maier-Hein K
AU  - Zenk M
AU  - Bendszus M
AU  - Wick W
AU  - Calabrese E
AU  - Rudie J
AU  - Villanueva-Meyer J
AU  - Cha S
AU  - Ingalhalikar M
AU  - Jadhav M
AU  - Pandey U
AU  - Saini J
AU  - Garrett J
AU  - Larson M
AU  - Jeraj R
AU  - Currie S
AU  - Frood R
AU  - Fatania K
AU  - Huang RY
AU  - Chang K
AU  - Balaña C
AU  - Capellades J
AU  - Puig J
AU  - Trenkler J
AU  - Pichler J
AU  - Necker G
AU  - Haunschmidt A
AU  - Meckel S
AU  - Shukla G
AU  - Liem S
AU  - Alexander GS
AU  - Lombardo J
AU  - Palmer JD
AU  - Flanders AE
AU  - Dicker AP
AU  - Sair HI
AU  - Jones CK
AU  - Venkataraman A
AU  - Jiang M
AU  - So TY
AU  - Chen C
AU  - Heng PA
AU  - Dou Q
AU  - Kozubek M
AU  - Lux F
AU  - Michálek J
AU  - Matula P
AU  - Keřkovský M
AU  - Kopřivová T
AU  - Dostál M
AU  - Vybíhal V
AU  - Vogelbaum MA
AU  - Mitchell JR
AU  - Farinhas J
AU  - Maldjian JA
AU  - Yogananda CGB
AU  - Pinho MC
AU  - Reddy D
AU  - Holcomb J
AU  - Wagner BC
AU  - Ellingson BM
AU  - Cloughesy TF
AU  - Raymond C
AU  - Oughourlian T
AU  - Hagiwara A
AU  - Wang C
AU  - To MS
AU  - Bhardwaj S
AU  - Chong C
AU  - Agzarian M
AU  - Falcão AX
AU  - Martins SB
AU  - Teixeira BCA
AU  - Sprenger F
AU  - Menotti D
AU  - Lucio DR
AU  - LaMontagne P
AU  - Marcus D
AU  - Wiestler B
AU  - Kofler F
AU  - Ezhov I
AU  - Metz M
AU  - Jain R
AU  - Lee M
AU  - Lui YW
AU  - McKinley R
AU  - Slotboom J
AU  - Radojewski P
AU  - Meier R
AU  - Wiest R
AU  - Murcia D
AU  - Fu E
AU  - Haas R
AU  - Thompson J
AU  - Ormond DR
AU  - Badve C
AU  - Sloan AE
AU  - Vadmal V
AU  - Waite K
AU  - Colen RR
AU  - Pei L
AU  - Ak M
AU  - Srinivasan A
AU  - Bapuraj JR
AU  - Rao A
AU  - Wang N
AU  - Yoshiaki O
AU  - Moritani T
AU  - Turk S
AU  - Lee J
AU  - Prabhudesai S
AU  - Morón F
AU  - Mandel J
AU  - Kamnitsas K
AU  - Glocker B
AU  - Dixon LVM
AU  - Williams M
AU  - Zampakis P
AU  - Panagiotopoulos V
AU  - Tsiganos P
AU  - Alexiou S
AU  - Haliassos I
AU  - Zacharaki EI
AU  - Moustakas K
AU  - Kalogeropoulou C
AU  - Kardamakis DM
AU  - Choi YS
AU  - Lee SK
AU  - Chang JH
AU  - Ahn SS
AU  - Luo B
AU  - Poisson L
AU  - Wen N
AU  - Tiwari P
AU  - Verma R
AU  - Bareja R
AU  - Yadav I
AU  - Chen J
AU  - Kumar N
AU  - Smits M
AU  - van der Voort SR
AU  - Alafandi A
AU  - Incekara F
AU  - Wijnenga MMJ
AU  - Kapsas G
AU  - Gahrmann R
AU  - Schouten JW
AU  - Dubbink HJ
AU  - Vincent AJPE
AU  - van den Bent MJ
AU  - French PJ
AU  - Klein S
AU  - Yuan Y
AU  - Sharma S
AU  - Tseng TC
AU  - Adabi S
AU  - Niclou SP
AU  - Keunen O
AU  - Hau AC
AU  - Vallières M
AU  - Fortin D
AU  - Lepage M
AU  - Landman B
AU  - Ramadass K
AU  - Xu K
AU  - Chotai S
AU  - Chambless LB
AU  - Mistry A
AU  - Thompson RC
AU  - Gusev Y
AU  - Bhuvaneshwar K
AU  - Sayah A
AU  - Bencheqroun C
AU  - Belouali A
AU  - Madhavan S
AU  - Booth TC
AU  - Chelliah A
AU  - Modat M
AU  - Shuaib H
AU  - Dragos C
AU  - Abayazeed A
AU  - Kolodziej K
AU  - Hill M
AU  - Abbassy A
AU  - Gamal S
AU  - Mekhaimar M
AU  - Qayati M
AU  - Reyes M
AU  - Park JE
AU  - Yun J
AU  - Kim HS
AU  - Mahajan A
AU  - Muzi M
AU  - Benson S
AU  - Beets-Tan RGH
AU  - Teuwen J
AU  - Herrera-Trujillo A
AU  - Trujillo M
AU  - Escobar W
AU  - Abello A
AU  - Bernal J
AU  - Gómez J
AU  - Choi J
AU  - Baek S
AU  - Kim Y
AU  - Ismael H
AU  - Allen B
AU  - Buatti JM
AU  - Kotrotsou A
AU  - Li H
AU  - Weiss T
AU  - Weller M
AU  - Bink A
AU  - Pouymayou B
AU  - Shaykh HF
AU  - Saltz J
AU  - Prasanna P
AU  - Shrestha S
AU  - Mani KM
AU  - Payne D
AU  - Kurc T
AU  - Pelaez E
AU  - Franco-Maldonado H
AU  - Loayza F
AU  - Quevedo S
AU  - Guevara P
AU  - Torche E
AU  - Mendoza C
AU  - Vera F
AU  - Ríos E
AU  - López E
AU  - Velastin SA
AU  - Ogbole G
AU  - Soneye M
AU  - Oyekunle D
AU  - Odafe-Oyibotha O
AU  - Osobu B
AU  - Shu'aibu M
AU  - Dorcas A
AU  - Dako F
AU  - Simpson AL
AU  - Hamghalam M
AU  - Peoples JJ
AU  - Hu R
AU  - Tran A
AU  - Cutler D
AU  - Moraes FY
AU  - Boss MA
AU  - Gimpel J
AU  - Veettil DK
AU  - Schmidt K
AU  - Bialecki B
AU  - Marella S
AU  - Price C
AU  - Cimino L
AU  - Apgar C
AU  - Shah P
AU  - Menze B
AU  - Barnholtz-Sloan JS
AU  - Martin J
AU  - Bakas S
PY  - 2022
JO  - Nature communications
DO  - 10.1038/s41467-022-33407-5
UR  - https://doi.org/10.1038/s41467-022-33407-5
ER  -

APA

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