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Kiechle, J. ; Fischer, S.M. ; Lang, D.M. ; di Folco, M. ; Foreman, S.C.* ; Rösner, V.K.N.* ; Lohse, A.K.* ; Mogler, C.* ; Knebel, C.* ; Makowski, M.R.* ; Woertler, K.* ; Combs, S.E.* ; Gersing, A.S.* ; Peeken, J.C. ; Schnabel, J.A.

Unifying local and global shape descriptors to grade soft-tissue sarcomas using graph convolutional networks.

In: (Proceedings - International Symposium on Biomedical Imaging). 345 E 47th St, New York, Ny 10017 Usa: Ieee, 2024. DOI: 10.1109/ISBI56570.2024.10635799 (Proceedings - International Symposium on Biomedical Imaging)
DOI
The tumor grading of patients suffering from soft-tissue sarcomas is a critical task, as an accurate classification of this high-mortality cancer entity constitutes a decisive factor in devising optimal treatment strategies. In this work, we focus on distinguishing soft-tissue sarcoma subtypes solely based on their 3D morphological characteristics, derived from tumor segmentation masks. Notably, we direct attention to overcoming the limitations of texture-based methodologies, which often fall short of providing adequate shape delineation. To this end, we propose a novel yet elegant modular geometric deep learning framework coined Global Local Graph Convolutional Network (GloLo-GCN) that integrates local and global shape characteristics into a meaningful unified shape descriptor. Evaluated on a multi-center dataset, our proposed model performs better in soft-tissue sarcoma grading than GCNs based on state-of-the-art graph convolutions and a volumetric 3D convolutional neural network, also evaluated on binary segmentation masks exclusively.
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Publication type Article: Conference contribution
Corresponding Author
Keywords Gcns ; Shape Analysis ; Tumor Grading
ISSN (print) / ISBN 1945-7928
e-ISSN 1945-8452
Conference Title Proceedings - International Symposium on Biomedical Imaging
Publisher Ieee
Publishing Place 345 E 47th St, New York, Ny 10017 Usa
Non-patent literature Publications
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
Institute of Radiation Medicine (IRM)
Grants Munich Center for Machine Learning
Konrad Zuse Schools of Excellence in Reliable AI
Wilhelm Sander Foundation in Cancer Research