Related Experiment Video
Updated: May 7, 2025
07:15
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
6.6K
Multimodal deep learning approaches for precision oncology: a comprehensive review.
Huan Yang1, Minglei Yang2, Jiani Chen1,3
1Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Chengdian Road, Kecheng District, Quzhou 324000, Zhejiang, China.
Briefings in Bioinformatics
|January 5, 2025
Summary
Multimodal deep learning (MDL) integrates diverse cancer data for precision oncology. This review synthesizes MDL applications, methods, and datasets, guiding future research in cancer care.
Area of Science:
- Oncology
- Artificial Intelligence
- Biomedical Data Science
Background:
- Large-scale biomedical data in oncology is rapidly accumulating.
- Deep learning (DL) technologies have advanced significantly.
- Multimodal DL (MDL) is emerging as a key tool in precision oncology.
Purpose of the Study:
- To provide a comprehensive overview of MDL applications in precision oncology.
- To synthesize findings from an extensive literature survey of 651 articles.
- To identify current limitations and future research directions.
Main Methods:
- Literature survey of 651 articles published before September 2024.
- Outline of publicly available multimodal datasets for cancer research.
- Discussion of DL training methods, data representation, and fusion strategies.
Main Results:
- MDL is applied in tumor segmentation, detection, diagnosis, prognosis, treatment selection, and therapy response monitoring.
- Key DL training methods, data representation techniques, and fusion strategies are discussed.
- Publicly available multimodal datasets supporting cancer research are identified.
Conclusions:
- MDL is crucial for advancing precision oncology.
- Current MDL approaches have limitations that require further investigation.
- Future research should focus on leveraging MDL to overcome challenges and enhance cancer care.
