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References

[1] Chato, L., & Latifi, S. (2017). Machine Learning and Deep Learning Techniques to Predict Overall Survival of Brain Tumor Patients using MRI Images. 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE). doi:10.1109/bibe.2017.00-86

[2] Lao, J., Chen, Y., Li, Z., Li, Q., Zhang, J., Liu, J., & Zhai, G. (2017). A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme. Scientific Reports,7(1). doi:10.1038/s41598-017-10649-8

[3] Zacharaki, E. I., Wang, S., Chawla, S., Yoo, D. S., Wolf, R., Melhem, E. R., & Davatzikos, C. (2009). Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magnetic Resonance in Medicine,62(6), 1609-1618. doi:10.1002/mrm.22147

[4] Albertina, B., Watson, M., Holback, C., Jarosz, R., Kirk, S., Lee, Y., … Lemmerman, J. (2016). Radiology Data from The Cancer Genome Atlas Lung Adenocarcinoma [TCGA-LUAD] collection. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2016.JGNIHEP5

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