Application of deep learning-based multimodal fusion technology in cancer diagnosis: A survey

Yan Li, Liangrui Pan, Yijun Peng, Xiaoyu Li, Xiang Wang, Limeng Qu, Qiya Song, Qingchun Liang, Shaoliang Peng

Abstract: Relying solely on a single medical data for cancer diagnosis may increase the risk of misdiagnosis and missed diagnosis. Multi-modal data provides comprehensive information on disease characteristics and can effectively promote the development of precision oncology. This paper first introduces the genomic, pathological, radiological and clinical information in cancer multimodal data. Secondly, the common subfields of cancer multimodal data fusion are reviewed, with emphasis on data fusion techniques. The evolution of architectures under different fusion classes is compared, highlighting their comparative advantages and limitations. Importantly, we systematically reviewed the last five years of deep learning-based multimodal cancer data fusion, focusing on the application of multimodal techniques to cancer survival prediction and subtype typing. Finally, we present the challenges and possible solutions for multimodal applications in cancer. The purpose of this paper is to promote the fusion and application of multimodal tumor data and highlight potential research directions in the future.

External IDs:dblp:journals/eaai/LiPPLWQSLP25