M. Jorge Cardoso; Tal Arbel; Su-Lin Lee; Veronika Cheplygina; Simone Balocco; Diana Mateus; Guillaume Zahnd; Maier-Hein Springer International Publishing AG (2017) Pehmeäkantinen kirja
M. Jorge Cardoso; Tal Arbel; Andrew Melbourne; Hrvoje Bogunovic; Pim Moeskops; Xinjian Chen; Ernst Schwartz; Mona Garvin Springer International Publishing AG (2017) Pehmeäkantinen kirja
M. Jorge Cardoso; Tal Arbel; Fei Gao; Bernhard Kainz; Theo Van Walsum; Kuangyu Shi; Kanwal K. Bhatia; Roman Peter; Verca Springer International Publishing AG (2017) Pehmeäkantinen kirja
M. Jorge Cardoso; Tal Arbel; João Manuel R.S. Tavares; Stephen Aylward; Shuo Li; Emad Boctor; Gabor Fichtinger; K Cleary Springer International Publishing AG (2017) Pehmeäkantinen kirja
M. Jorge Cardoso; Tal Arbel; Xiongbiao Luo; Stefan Wesarg; Tobias Reichl; Miguel Ángel González Ballester; Jonatha McLeod Springer International Publishing AG (2017) Pehmeäkantinen kirja
M. Jorge Cardoso; Tal Arbel; Gustavo Carneiro; Tanveer Syeda-Mahmood; Joao Manuel R. S. Tavares; Mehdi Moradi; An Bradley Springer International Publishing AG (2017) Pehmeäkantinen kirja
M. Jorge Cardoso; Tal Arbel; Enzo Ferrante; Xavier Pennec; Adrian Dalca; Sarah Parisot; Sarang Joshi; Nema Batmanghelich Springer International Publishing AG (2017) Pehmeäkantinen kirja
Henning Müller; B. Michael Kelm; Tal Arbel; Weidong Cai; M. Jorge Cardoso; Georg Langs; Bjoern Menze; Dimitris Metaxas Springer International Publishing AG (2017) Pehmeäkantinen kirja
Qian Wang; Fausto Milletari; Hien V. Nguyen; Shadi Albarqouni; M. Jorge Cardoso; Nicola Rieke; Ziyue Xu; Konst Kamnitsas Springer Nature Switzerland AG (2019) Pehmeäkantinen kirja
Cristina Oyarzun Laura; M. Jorge Cardoso; Michal Rosen-Zvi; Georgios Kaissis; Marius George Linguraru; Raj Shekhar; Wesarg Springer Nature Switzerland AG (2021) Pehmeäkantinen kirja
Lisa Koch (ed.); M. Jorge Cardoso (ed.); Enzo Ferrante (ed.); Konstantinos Kamnitsas (ed.); Mobarakol Islam (ed.); M Jiang Springer (2023) Pehmeäkantinen kirja
This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2014, held in Cambridge, MA, USA, in September 2014 as a satellite event of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014. The 11 revised full papers presented were carefully reviewed and selected from numerous submissions with a key aspect on probabilistic modeling applied to medical image analysis. The objectives of this workshop compared to other workshops, e.g. machine learning in medical imaging, have a stronger mathematical focus on the foundations of probabilistic modeling and inference. The papers highlight the potential of using Bayesian or random field graphical models for advancing scientific research in biomedical image analysis or for the advancement of modeling and analysis of medical imaging data.
