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Open Access
Issue
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
Article Number 02025
Number of page(s) 7
Section Machine Learning Applications in Vision, Security, and Healthcare
DOI https://doi.org/10.1051/itmconf/20257802025
Published online 08 September 2025
  1. Bollschweiler, E.: 'Benefits and limitations of Kaplan-Meier calculations of survival chance in cancer surgery', Langenbeck's Arch. Surg., 2003, 388, pp. 239–244 [Google Scholar]
  2. Zhu, W., Xie, L., Han, J., Guo, X.: 'The application of deep learning in cancer prognosis prediction', Cancers, 2020, 12, (3), pp. 603 [Google Scholar]
  3. Wulczyn, E., Steiner, D.F., Xu, Z., et al.: 'Deep learning-based survival prediction for multiple cancer types using histopathology images', PLoS One, 2020, 15, (6), pp. e0233678 [Google Scholar]
  4. She, Y., Jin, Z., Wu, J., et al.: 'Development and validation of a deep learning model for non-small cell lung cancer survival', JAMA Netw. Open, 2020, 3, (6), pp. e205842 [Google Scholar]
  5. Hao, J., Kim, Y., Kim, T.K., Kang, M.: 'PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data', BMC Bioinformatics, 2018, 19, (1), pp. 510 [Google Scholar]
  6. Acs, B., Rantalainen, M., Hartman, J.: 'Artificial intelligence as the next step towards precision pathology', J. Intern. Med., 2020, 288, (1), pp. 62–81 [Google Scholar]
  7. Bejnordi, B.E., Veta, M., Van Diest, P.J., et al.: 'Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer', JAMA, 2017, 318, (22), pp. 2199–2210 [CrossRef] [Google Scholar]
  8. Giunchiglia, E., Nemchenko, A., van der Schaar, M.: 'RNN-Surv: A deep recurrent model for survival analysis', Proc. Int. Conf. Artif. Neural Netw. (ICANN), Rhodes, Greece, Oct. 2018, pp. 23–32 [Google Scholar]
  9. Vale-Silva, L.A., Rohr, K.: 'Long-term cancer survival prediction using multimodal deep learning', Sci. Rep., 2021, 11, (1), pp. 13505 [Google Scholar]
  10. Chato, L., Latifi, S.: 'Machine learning and deep learning techniques to predict overall survival of brain tumor patients using MRI images', Proc. IEEE Int. Conf. Bioinf. Biomed. Eng. (BIBE), Kansas City, USA, Oct. 2017, pp. 9–14 [Google Scholar]
  11. Mobadersany, P., Yousefi, S., Amgad, M., et al.: 'Predicting cancer outcomes from histology and genomics using convolutional networks', Proc. Natl. Acad. Sci. U.S.A., 2018, 115, (13), pp. E2970–E2979 [Google Scholar]
  12. van der Velden, B.H.M., Kuijf, H.J., Gilhuijs, K.G.A., Viergever, M.A.: 'Explainable artificial intelligence (XAI) in deep learning-based medical image analysis', Med. Image Anal., 2022, 79, pp. 102470 [Google Scholar]
  13. Antwarg, L., Miller, R.M., Shapira, B., Rokach, L.: 'Explaining anomalies detected by autoencoders using Shapley Additive Explanations', Expert Systems with Applications, 2021, 186, 115736 [Google Scholar]

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