arXiv Preprint: http://arxiv.org/abs/2212.06515

• We thank CLAM's team [1] for contributing such an easy-to-use repo for WSI preprocessing,
• and NLST [2] and TCGA [3] for making WSI datasets publicly-available to facilitate cancer research,
• and DATE [4] for providing the demo to train basic DATE models on clinical tabular data,
• and all the authors of DeepAttnMISL [5], PatchGCN [6], and ESAT [7,8] for contributing their code to the community.

[1] Lu, M. Y.; Williamson, D. F.; Chen, T. Y.; Chen, R. J.; Bar- bieri, M.; and Mahmood, F. 2021. Data-efficient and weakly supervised computational pathology on whole-slide images. Nature biomedical engineering, 5(6): 555–570.

[2] Team, N. L. S. T. R. 2011. The national lung screening trial: overview and study design. Radiology, 258(1): 243–53.

[3] Kandoth, C.; McLellan, M. D.; Vandin, F.; Ye, K.; Niu, B.; Lu, C.; Xie, M.; Zhang, Q.; McMichael, J. F.; Wycza- lkowski, M. A.; Leiserson, M. D. M.; Miller, C. A.; Welch, J. S.; Walter, M. J.; Wendl, M. C.; Ley, T. J.; Wilson, R. K.; Raphael, B. J.; and Ding, L. 2013. Mutational landscape and significance across 12 major cancer types. Nature, 502: 333 – 339.

[4] Chapfuwa, P.; Tao, C.; Li, C.; Page, C.; Goldstein, B.; Duke, L. C.; and Henao, R. 2018. Adversarial time-to-event mod- eling. In International Conference on Machine Learning, 735–744. PMLR.

[5] Yao, J.; Zhu, X.; Jonnagaddala, J.; Hawkins, N.; and Huang, J. 2020. Whole slide images based cancer survival predic- tion using attention guided deep multiple instance learning networks. Medical Image Analysis, 65: 101789.

[6] Chen, R. J.; Lu, M. Y.; Shaban, M.; Chen, C.; Chen, T. Y.; Williamson, D. F.; and Mahmood, F. 2021. Whole Slide Im- ages are 2D Point Clouds: Context-Aware Survival Predic- tion using Patch-based Graph Convolutional Networks. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 339–349. Springer.

[7] Shen, Y.; Liu, L.; Tang, Z.; Chen, Z.; Ma, G.; Dong, J.; Zhang, X.; Yang, L.; and Zheng, Q. 2022. Explainable Survival Analysis with Convolution-Involved Vision Transformer. In Proceedings of the AAAI Conference on Artificial Intelligence, 2207--2215. AAAI Press.

[8] Liu, P.; Fu, B.; Ye, F.; Yang, R.; Xu, B.; and Ji, L. 2022. Dual-Stream Transformer with Cross-Attention on Whole- Slide Image Pyramids for Cancer Prognosis. arXiv preprint arXiv:2206.05782.