Survey on natural language processing in medical image analysis.
10.11817/j.issn.1672-7347.2022.220376
- Author:
Zhengliang LIU
1
;
Mengshen HE
2
;
Zuowei JIANG
3
;
Zihao WU
4
;
Haixing DAI
4
;
Lian ZHANG
5
;
Siyi LUO
6
;
Tianle HAN
2
;
Xiang LI
7
;
Xi JIANG
8
;
Dajiang ZHU
9
;
Xiaoyan CAI
3
;
Bao GE
2
;
Wei LIU
5
;
Jun LIU
10
;
Dinggang SHEN
11
;
Tianming LIU
4
Author Information
1. Department of Computer Science, University of Georgia, Athens, GA 30602, USA. zl18864@uga.edu.
2. School of Physics & Information Technology, Shaanxi Normal University, Xi'an 710119, China.
3. School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
4. Department of Computer Science, University of Georgia, Athens, GA 30602, USA.
5. Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA.
6. Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
7. Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
8. School of Life Science and Technology, University of Electronic Science and Technology, Chengdu 611731, China.
9. Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.
10. Department of Radiology, Second Xiangya Hospital, Central South University, Changsha 410011, China.
11. School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China.
- Publication Type:Journal Article
- Keywords:
deep learning;
medical imaging;
multimodal input;
natural language processing
- MeSH:
Humans;
Natural Language Processing;
Surveys and Questionnaires
- From:
Journal of Central South University(Medical Sciences)
2022;47(8):981-993
- CountryChina
- Language:English
-
Abstract:
Recent advancement in natural language processing (NLP) and medical imaging empowers the wide applicability of deep learning models. These developments have increased not only data understanding, but also knowledge of state-of-the-art architectures and their real-world potentials. Medical imaging researchers have recognized the limitations of only targeting images, as well as the importance of integrating multimodal inputs into medical image analysis. The lack of comprehensive surveys of the current literature, however, impedes the progress of this domain. Existing research perspectives, as well as the architectures, tasks, datasets, and performance measures examined in the present literature, are reviewed in this work, and we also provide a brief description of possible future directions in the field, aiming to provide researchers and healthcare professionals with a detailed summary of existing academic research and to provide rational insights to facilitate future research.
