A systematic review of intermediate fusion in multimodal deep learning for biomedical applications

Deep learning has revolutionized biomedical research by providing sophisticated methods to handle complex, high-dimensional data. Multimodal deep learning (MDL) further enhances this capability by integrating diverse data types such as imaging, textual data, and genetic information, leading to more robust and accurate predictive models. In MDL, differently from early and late fusion methods, intermediate fusion stands out for its ability to effectively combine modality-specific features during the learning process. This systematic review comprehensively analyzes and formalizes current intermediate fusion methods in biomedical applications, highlighting their effectiveness in improving predictive performance…

• EC
  European Commission
• UC
  Università Campus Bio-Medico di Roma
• K
  Kempestiftelserna

  Award: JCSMK24-0094
• MD
  Ministero dell'Istruzione e del Merito

  Award: PE0000013-FAIR