Prasanna, Prateek

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Number of items: 4.

Journal Article

Pati, Sarthak; Baid, Ujjwal; Edwards, Brandon; Sheller, Micah; Wang, Shih-Han; Reina, G Anthony; Foley, Patrick; Gruzdev, Alexey; Karkada, Deepthi; Davatzikos, Christos; Sako, Chiharu; Ghodasara, Satyam; Bilello, Michel; Mohan, Suyash; Vollmuth, Philipp; Brugnara, Gianluca; Preetha, Chandrakanth J; Sahm, Felix; Maier-Hein, Klaus; Zenk, Maximilian; ... (2023). Author Correction: Federated learning enables big data for rare cancer boundary detection. Nature communications, 14(1), p. 436. Nature Publishing Group 10.1038/s41467-023-36188-7

Pati, Sarthak; Baid, Ujjwal; Edwards, Brandon; Sheller, Micah; Wang, Shih-Han; Reina, G Anthony; Foley, Patrick; Gruzdev, Alexey; Karkada, Deepthi; Davatzikos, Christos; Sako, Chiharu; Ghodasara, Satyam; Bilello, Michel; Mohan, Suyash; Vollmuth, Philipp; Brugnara, Gianluca; Preetha, Chandrakanth J; Sahm, Felix; Maier-Hein, Klaus; Zenk, Maximilian; ... (2022). Federated learning enables big data for rare cancer boundary detection. Nature communications, 13(1), p. 7346. Nature Publishing Group 10.1038/s41467-022-33407-5

Ding, Ruiwen; Prasanna, Prateek; Corredor, Germán; Barrera, Cristian; Zens, Philipp; Lu, Cheng; Velu, Priya; Leo, Patrick; Beig, Niha; Li, Haojia; Toro, Paula; Berezowska, Sabina; Baxi, Vipul; Balli, David; Belete, Merzu; Rimm, David L; Velcheti, Vamsidhar; Schalper, Kurt; Madabhushi, Anant (2022). Image analysis reveals molecularly distinct patterns of TILs in NSCLC associated with treatment outcome. NPJ precision oncology, 6(1), p. 33. Springer Nature 10.1038/s41698-022-00277-5

Lu, Cheng; Bera, Kaustav; Wang, Xiangxue; Prasanna, Prateek; Xu, Jun; Janowczyk, Andrew; Beig, Niha; Yang, Michael; Fu, Pingfu; Lewis, James; Choi, Humberto; Schmid, Ralph; Berezowska, Sabina; Schalper, Kurt; Rimm, David; Velcheti, Vamsidhar; Madabhushi, Anant (2020). A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study. The Lancet. Digital health, 2(11), e594-e606. Elsevier 10.1016/s2589-7500(20)30225-9

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