Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 02026 | |
| Number of page(s) | 9 | |
| Section | Machine Learning Applications in Vision, Security, and Healthcare | |
| DOI | https://doi.org/10.1051/itmconf/20257802026 | |
| Published online | 08 September 2025 | |
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- Vale-Silva, L.A., Rohr, K.: 'Long-term cancer survival prediction using multimodal deep learning', Sci. Rep., 2021, 11, (1), pp. 13505 [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- Mahmoud, Y., Horvath, K., Zhou, Y.: 'Deep learning for predicting rehabilitation success: Advancing clinical and patient-reported outcome modeling', Electronics, 2025, 14 (6), 1082 [Google Scholar]
- Zhang, Y., Cao, G., Chen, J., Yuan, Y., Li, L., Tan, D., Ling, Z.: 'GAIT Time Parameter Analysis-Based Rehabilitation Evaluation System of Lower Limb Motion Function', Lect. Notes Comput. Sci., 2022, pp. 90–102 [Google Scholar]
- Cai, S., Chen, Y., Huang, S., Wu, Y., Zheng, H., Li, X., Xie, L.: 'SVM-based classification of sEMG signals for upper-limb self-rehabilitation training', Front. Neurorobot., 2019, 13, 31 [Google Scholar]
- Miyasaka, H., et al.: 'A study of the training method of sub-acute stroke patients of the upper extremity: decision tree analysis', Jpn. J. Compr. Rehabil. Sci., 2014, 5, pp. 117–124 [Google Scholar]
- Ghonchi, H., et al.: 'Deep recurrent-convolutional neural network for classification of simultaneous EEG-fNIRS signals', IET Image Process., 2020, 14, pp. 142–153 [Google Scholar]
- Gao, M., Mao, J.: 'A novel active rehabilitation model for stroke patients using electroencephalography signals and deep learning technology', Front. Neurosci., 2021, 15 [Google Scholar]
- Deng, J., et al.: 'Imagenet: A large-scale hierarchical image database', Proc. 2009 IEEE Conf. Comput. Vis. Pattern Recognit., 2009, pp. 248–255 [Google Scholar]
- Hochreiter, S., Schmidhuber, J.: 'Long short-term memory', Neural Comput., 1997, 9 (8), pp. 1735–1780 [CrossRef] [Google Scholar]
- Lugaresi, C., et al.: 'Mediapipe: A framework for building perception pipelines', arXiv preprint, arXiv:1906.08172, 2019 [Google Scholar]
- Shi, L., et al.: 'Detection of rehabilitation training effect of upper limb movement disorder based on MPL-CNN', Sensors, 2024, 24 (4), 1105 [Google Scholar]
- Zaremba, W., Sutskever, I., Vinyals, O.: 'Recurrent neural network regularization', arXiv preprint, arXiv:1409.2329, 2014 [Google Scholar]
- Graves, A.: 'Generating sequences with recurrent neural networks', arXiv preprint, arXiv:1308.0850, 2013 [Google Scholar]
- Kingma, D.P., Ba, J.: 'Adam: A method for stochastic optimization', arXiv preprint, arXiv:1412.6980, 2015 [Google Scholar]
- Li, X., Zhou, P., Aruin, A.S.: 'Teager-Kaiser energy operation of surface EMG improves muscle activity onset detection', Ann. Biomed. Eng., 2007, 35 (9), pp. 1532–1538 [Google Scholar]
- Bonato, P., D’Alessio, T., Knaflitz, M.: 'A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait', IEEE Trans. Biomed. Eng., 1998, 45 (3), pp. 287–299 [Google Scholar]
- Ghislieri, M., et al.: 'Long short-term memory (LSTM) recurrent neural network for muscle activity detection', J. NeuroEng. Rehabil., 2021, 18 (1) [Google Scholar]
- Singh, R.M., Chatterji, S., Kumar, A.: 'A review on surface EMG based control schemes of exoskeleton robot in stroke rehabilitation', Proc. Int. Conf. Mach. Intell. Res. Adv., 2014, pp. 310–315 [Google Scholar]
- Li, X., et al.: 'Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond', arXiv preprint, arXiv:2103.10689, 2021 [Google Scholar]
- Zhang, X., Rong, X., Luo, H.: 'Optimizing lower limb rehabilitation: The intersection of machine learning and rehabilitative robotics', Front. Rehabil. Sci., 2024, 5 [Google Scholar]
- Amann, J., et al.: 'Explainability for artificial intelligence in healthcare: A multidisciplinary perspective', BMC Med. Inform. Decis. Mak., 2020, 20 [Google Scholar]
- Yang, F., et al.: 'Action recognition in rehabilitation: Combining 3D convolution and LSTM with spatiotemporal attention', Front. Physiol., 2024, 15 [Google Scholar]
- Jiao, W., et al.: 'Activity recognition in rehabilitation training based on ensemble stochastic configuration networks', Neural Comput. Appl., 2023, 35, pp. 21229–21245 [Google Scholar]
- Rudin, C.: 'Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead', Nat. Mach. Intell., 2019, 1 (5), pp. 206–215 [Google Scholar]
- Zhang, C., et al.: 'Interpretable dual-branch EMGNet: A transfer learning-based network for inter-subject lower limb motion intention recognition', Eng. Appl. Artif. Intell., 2023, 130, 107761 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

