Author: Guillaume Chassagnon; Maria Vakalopoulou; Enzo Battistella; Stergios Christodoulidis; Trieu-Nghi Hoang-Thi; Severine Dangeard; Eric Deutsch; Fabrice Andre; Enora Guillo; Nara Halm; Stefany El Hajj; Florian Bompard; Sophie Neveu; Chahinez Hani; Ines Saab; Alienor Campredon; Hasmik Koulakian; Souhail Bennani; Gael Freche; Aurelien Lombard; Laure Fournier; Hippolyte Monnier; Teodor Grand; Jules Gregory; Antoine Khalil; Elyas Mahdjoub; Pierre-Yves Brillet; Stephane Tran Ba; Valerie Bousson; Marie-Pierre Revel; Nikos Paragios
Title: AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia Document date: 2020_4_22
ID: nxm1jr0x_12
Snippet: For the COVID-19 radiological pattern segmentation part, 50 patients from 3 centers (A: 20 patients; B: 15 patients, C: 15 patients) were included to compose a training and validation dataset, 130 patients from the remaining 3 centers (D: 50 patients; E: 50 patients, F: 30 patients) were included to compose the test dataset ( Table 2 ). The proportion between the CT manufacturers in the datasets was pre-determined in order to maximize the model g.....
Document: For the COVID-19 radiological pattern segmentation part, 50 patients from 3 centers (A: 20 patients; B: 15 patients, C: 15 patients) were included to compose a training and validation dataset, 130 patients from the remaining 3 centers (D: 50 patients; E: 50 patients, F: 30 patients) were included to compose the test dataset ( Table 2 ). The proportion between the CT manufacturers in the datasets was pre-determined in order to maximize the model generalizability while taking into account the data distribution.
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