Semantic and Visual Attention-Driven Multi-LSTM Network for Automated Clinical Report Generation-Reference-Cited by-同舟云学术

Semantic and Visual Attention-Driven Multi-LSTM Network for Automated Clinical Report Generation

Published:2024 Issue: Volume: Page:233-248
ISSN:1860-949X
Container-title:Studies in Computational Intelligence
language:en
Short-container-title:

Author:

Huang Cheng^ORCID,Shen Junhao^ORCID,Hu Beichen^ORCID,Ausaf Ali Haqqani Mohammad^ORCID,Lee Tsengdar^ORCID,Kooner Karanjit^ORCID,Zhang Ning^ORCID,Zhang Jia^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-63592-2_18

Reference39 articles.

1. P. Anderson, X. He, C. Buehler, D. Teney, M. Johnson, S. Gould and L. Zhang.: Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 6077–6086 (2018).

2. F. Ali, H. Mohsen, S. Mohammad Amin, Y. Peter, R. Cyrus, H. Julia and F. David.: Every Picture Tells a Story: Generating Sentences from Images. Proceedings of the 11th European Conference on Computer Vision: Part IV, pp. 15–29 (2020).

3. X. Zhou, Y. Li and W. Liang.: CNN-RNN Based Intelligent Recommendation for Online Medical Pre-Diagnosis Support. IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol: 18, pp. 912–921 (2020).

4. J. Maitre, K. Bouchard and S. Gaboury.: Fall Detection With UWB Radars and CNN-LSTM Architecture. IEEE Journal of Biomedical and Health Informatics, vol: 25, pp. 1273–1283 (2020).

5. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, Aidan N. Gomez, Ł. Kaiser and I. Polosukhin.: Attention Is All You Need. Proceedings of the 31st Conference on Neural Information Processing Systems (NeurIPS) (2017).