Multimodal data integration for oncology in the era of deep neural networks: a review
Moffitt Cancer Center · Rowan University
Abstract
Cancer research encompasses data across various scales, modalities, and resolutions, from screening and diagnostic imaging to digitized histopathology slides to various types of molecular data and clinical records. The integration of these diverse data types for personalized cancer care and predictive modeling holds the promise of enhancing the accuracy and reliability of cancer screening, diagnosis, and treatment. Traditional analytical methods, which often focus on isolated or unimodal information, fall short of capturing the complex and heterogeneous nature of cancer data. The advent of deep neural networks has spurred the development of sophisticated multimodal data fusion techniques capable of extracting…
Citation impact
- FWCI
- 34.30
- Percentile
- 100%
- References
- 223
Authors
5Topics & keywords
- Data integration
- Deep learning
- Computer science
- Artificial intelligence
- Data science
- Multimodal learning
- Modalities
- Transformative learning