Abstract
Predicting brain tumor survival can aid physicians in better assessing the efficacy of treatments and adjusting treatment plans in clinical practices to enhance patient survival. Recently, deep learning techniques have attracted massive attention in predicting brain tumor survival. However, the majority of existing methods necessitate at least two or more independent networks for knowledge sharing later in the model and overlook the significance of population information. In this paper, we propose an end-to-end multi-model brain tumor survival prediction (MPSurv) model that incorporates patient population information. Moreover, given the presence of censored data, we propose to address this issue by generating pseudo-labels, which in turn augments the original data and improves the utilization of the dataset. We have collected and supplemented survival labels based on the BraTS 2021 dataset for the training and validation of segmentation and prediction tasks. Experimental results demonstrate that our model enhances the accuracy of brain tumor survival prediction and exhibits superior generalizability. The source code is available at: https://github.com/APTX574/MPSurv.
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Acknowledgements
This work was supported by the Zhejiang Provincial Natural Science Foundation of China (No. LY21F020017), National Natural Science Foundation of China (No.U20A20386, U22A2033), Chinese Key-Area Research and Development Program of Guangdong Province (2020B0101350001), GuangDong Basic and Applied Basic Research Foundation (No. 2022A1515110570), Innovation teams of youth innovation in science and technology of high education institutions of Shandong province (No. 2021KJ088), the Shenzhen Science and Technology Program (JCYJ20220818103001002), and the Guangdong Provincial Key Laboratory of Big Data Computing, The Chinese University of Hong Kong, Shenzhen.
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Wang, Q. et al. (2023). MPSurv: End-to-End Multi-model Pseudo-Label Model for Brain Tumor Survival Prediction with Population Information Integration. In: Qin, W., Zaki, N., Zhang, F., Wu, J., Yang, F., Li, C. (eds) Computational Mathematics Modeling in Cancer Analysis. CMMCA 2023. Lecture Notes in Computer Science, vol 14243. Springer, Cham. https://doi.org/10.1007/978-3-031-45087-7_13
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