Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review

arXiv:2303.06471v3 Announce Type: replace
Abstract: Cancer has relational information residing at varying scales, modalities, and resolutions of the acquired data, such as radiology, pathology, genomics, proteomics, and clinical records. Integrating diverse data types can improve the accuracy and reliability of cancer diagnosis and treatment. There can be disease-related information that is too subtle for humans or existing technological tools to discern visually. Traditional methods typically focus on partial or unimodal information about biological systems at individual scales and fail to …

abstract accuracy acquired arxiv cancer cancer diagnosis clinical cs.lg data data integration diagnosis diverse genomics information integration multimodal multimodal data networks neural networks oncology pathology proteomics radiology records relational reliability review treatment type types