학술논문
Lesion Graph Neural Networks for 2-Year Progression Free Survival Classification of Diffuse Large B-Cell Lymphoma Patients
Document Type
Conference
Author
Source
2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) Biomedical Imaging (ISBI), 2023 IEEE 20th International Symposium on. :1-5 Apr, 2023
Subject
Language
ISSN
1945-8452
Abstract
Survival analysis of DLBCL patients requires the interpretation of PET images characterised by multiple small lesions. Current machine-learning approaches addressing similar problems consider as input the cropped image of a single lesion or the whole volume. In this paper, we incorporate the information of all lesions by modeling their joint survival analysis with a graph learning approach. We propose a compact graph representation of the segmented lesions enriched by radiomics features and edge weights. The representation is fed to a graph attention network to predict the 2-year Progression-Free Survival of a DLBCL patient, formalised as a graph classification problem. Experimental results on a prospective clinical database with 583 patients show that our method improves over three baseline fusion approaches.