OCIF: automatically learning the optimized clinical information fusion method for computer-aided diagnosis tasks-Reference-Cited by-同舟云学术

OCIF: automatically learning the optimized clinical information fusion method for computer-aided diagnosis tasks

Published:2023-08-21 Issue:12 Volume:18 Page:2273-2286
ISSN:1861-6429
Container-title:International Journal of Computer Assisted Radiology and Surgery
language:en
Short-container-title:Int J CARS

Author:

Hu Zhaoyu,Li Leyin,Sui An,Wu Guoqing,Wang Yuanyuan,Shi Zhifeng,Yu Jinhua^ORCID,Chen Liang,Yang Guiguan,Sun Yuhao

Funder

National Natural Science Foundation of China

Science and Technology Commission of Fengxian District, Shanghai Municipality

Shanghai Municipal Science and Technology Major Project

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Radiology, Nuclear Medicine and imaging,General Medicine,Surgery,Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11548-023-02985-0.pdf

Reference33 articles.

1. Naser MA, Deen MJ (2020) Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2020.103758

2. Yu JH, Shi ZF, Lian YX, Li ZJ, Liu TT, Gao Y, Wang YY, Chen L, Mao Y (2017) Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol 27:3509–3522. https://doi.org/10.1007/s00330-016-4653-3

3. Feng X, Tustison NJ, Patel SH, Meyer CH (2020) Brain tumor segmentation using an ensemble of 3D U-nets and overall survival prediction using radiomic features. Front Comput Neurosc. https://doi.org/10.3389/fncom.2020.00025

4. Zhou T, Fu H, Zhang Y, Zhang C, Lu X, Shen J, Shao L (2020) M2Net: multi-modal multi-channel network for overall survival time prediction of brain tumor patients. In: Proceedings of the international conference on medical image computing and computer-assisted intervention, pp 221–231

5. Chato L, Kachroo P, Latifi S (2021) An automatic overall survival time prediction system for glioma brain tumor patients based on volumetric and shape features. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Brainles 2020) Pt Ii 12659:352–365. https://doi.org/10.1007/978-3-030-72087-2_31