Advancements in deep structured learning based medical image interpretation-Reference-Cited by-同舟云学术

Advancements in deep structured learning based medical image interpretation

Published:2022-07-04 Issue:5 Volume:43 Page:1131-1138
ISSN:0252-2667
Container-title:Journal of Information and Optimization Sciences
language:en
Short-container-title:Journal of Information and Optimization Sciences

Author:

Pasupathy Vijayalakshmi¹,Khilar Rashmita²

Affiliation:

1. Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India

2. Department of Information Technology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India

Publisher

Taru Publications

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/02522667.2022.2094550

Reference19 articles.

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3. Jue, J.; Jason, H.; Neelam, T.; Andreas, R.; Sean, B.L.; Joseph, D.O.; Harini, V. Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Shenzhen, China, 13–18 October 2019; pp. 221–229.

4. Borne, L.; Rivière, D.; Mangin, J.F. Combining 3D U-Net and bottom- up geometric constraints for automatic cortical sulci recognition. In Proceedings of the International Conference on Medical Imaging with Deep Learning, London, UK, 8–10 July 2019.

5. Casamitjana, A.; Catà, M.; Sánchez, I.; Combalia, M.; Vilaplana, V. Cascaded V-Net using ROI masks for brain tumor segmentation. In Proceedings of the International MICCAI Brainlesion Workshop, Quebec City, QC, Canada, 14 September 2017; pp. 381–391

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