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ALL-IN: A Local GLobal Graph-Based DIstillatioN Model for Representation Learning of Gigapixel Histopathology Images With Application In Cancer Risk Assessment

Title:	ALL-IN: A Local GLobal Graph-Based DIstillatioN Model for Representation Learning of Gigapixel Histopathology Images With Application In Cancer Risk Assessment
Authors:	Azadi, Puria^Aff14; Suderman, Jonathan^Aff14; Nakhli, Ramin^Aff14; Rich, Katherine^Aff14; Asadi, Maryam^Aff14; Kung, Sonia^Aff15; Oo, Htoo^Aff15; Keyes, Mira^Aff14; Farahani, Hossein^Aff14; MacAulay, Calum^Aff16; Goldenberg, Larry^Aff15; Black, Peter^Aff15; Bashashati, Ali^Aff14
Contributors:	Goos, Gerhard, Founding Editor^Aff1; Hartmanis, Juris, Founding Editor^Aff2; Bertino, Elisa, Editorial Board Member^Aff3; Gao, Wen, Editorial Board Member^Aff4; Steffen, Bernhard, Editorial Board Member^Aff5; Yung, Moti, Editorial Board Member^Aff6; Greenspan, Hayit, editor^Aff7; Madabhushi, Anant, editor^Aff8; Mousavi, Parvin, editor^Aff9; Salcudean, Septimiu, editor^Aff10; Duncan, James, editor^Aff11; Syeda-Mahmood, Tanveer, editor^Aff12; Taylor, Russell, editor^Aff13
Source:	Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 : 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part VI. 14225:765-775
Database:	Springer Nature eBooks