Graph-Based Multimodal Multi-lesion DLBCL Treatment Response Prediction from PET Images

Author:

Thiery OrianeORCID,Rizkallah MiraORCID,Bailly ClémentORCID,Bodet-Milin CarolineORCID,Itti EmmanuelORCID,Casasnovas René-OlivierORCID,Le Gouill StevenORCID,Carlier ThomasORCID,Mateus DianaORCID

Publisher

Springer Nature Switzerland

Reference18 articles.

1. Amyar, A., Ruan, S., Gardin, I., Chatelain, C., Decazes, P., Modzelewski, R.: 3-D RPET-NET: development of a 3-D PET imaging convolutional neural network for radiomics analysis and outcome prediction. IEEE TPRMS 3(2), 225–231 (2019)

2. Aswathi, A., et al.: Lesion graph neural networks for 2-year progression free survival classification of diffuse large B-cell lymphoma patients. In: IEEE International Symposium on Biomedical Imaging (ISBI) (2023)

3. Baltrušaitis, T., Ahuja, C., Morency, L.P.: Multimodal machine learning: a survey and taxonomy. IEEE TPAMI 41(2), 423–443 (2019)

4. Bhalodia, R., et al.: Improving pneumonia localization via cross-attention on medical images and reports. In: Med Image Comp and Comp Assisted Interventions (MICCAI) (2021)

5. Brody, S., Alon, U., Yahav, E.: How attentive are graph attention networks? In: International Conference on Learning Representations (2022)

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