Identification and Localization of Indolent and Aggressive Prostate Cancers Using Multilevel Bi-LSTM
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10278-024-01030-z.pdf
Reference39 articles.
1. Feng, Yujie, Fan Yang, Xichuan Zhou, Yanli Guo, Fang Tang, Fengbo Ren, Jishun Guo, and Shuiwang Ji. “A deep learning approach for targeted contrast-enhanced ultrasound-based prostate cancer detection.” IEEE/ACM Transactions on Computational Biology and Bioinformatics 16, no. 6 (2018): 1794-1801.
2. Iqbal, Saqib, Ghazanfar Farooq Siddiqui, Amjad Rehman, Lal Hussain, Tanzila Saba, Usman Tariq, and Adeel Ahmed Abbasi. “Prostate cancer detection using deep learning and traditional techniques.” IEEE Access 9 (2021): 27085–27100.
3. Arif, Muhammad, Ivo G. Schoots, Jose Castillo Tovar, Chris H. Bangma, Gabriel P. Krestin, Monique J. Roobol, WiroNiessen, and Jifke F. Veenland. “Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI.” European Radiology 30 (2020): 6582–6592.
4. Abbasi, Adeel Ahmed, Lal Hussain, Imtiaz Ahmed Awan, Imran Abbasi, Abdul Majid, Malik Sajjad Ahmed Nadeem, and Quratul-Ain Chaudhary. “Detecting prostate cancer using deep learning convolution neural network with transfer learning approach.” Cognitive Neurodynamics 14 (2020): 523–533.
5. Chen, Yingna, Chengdang Xu, Zhaoyu Zhang, Anqi Zhu, Xixi Xu, Jing Pan, Ying Liu, Denglong Wu, Shengsong Huang, and Qian Cheng. “Prostate cancer identification via photoacoustic spectroscopy and machine learning.” Photoacoustics 23 (2021): 100280.
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