A Survey on Deep Learning Methods for Cancer Diagnosis Using Multimodal Data Fusion
Chems Eddine Louahem M'Sabah, Ahmed Bouziane, Youcef Ferdi
Abstract
Recent advances in data collecting methods have enabled life scientists to collect multimodal data from a variety of biological application fields. Multimodal deep learning is becoming more widespread as the capacity of multimodal data streams and deep learning algorithms grows. This leads to the creation of models capable of consistently processing and interpreting multimodal data. The fusion of multimodal data, such as genetic data and histopathological images, is important to better understand cancer heterogeneity and complexity for specific therapies, as well as improving predictions for cancer research. In this brief overview, we present several pioneering deep learning models where we focus on different architectures that has seen use in the medical field, shedding some light on the importance of deep learning in cancer diagnosis considering its immense results in recent years. We particularly set our eyes on multimodal deep learning approaches fusing histopathological images and genomic data to achieve better results than a single modality.