Multimodality Overview

Principles

Definition

Multimodality is an interdisciplinary field of research and an academic concept dedicated to understanding how meaning is constructed and communicated through the dynamic combination and interaction of multiple semiotic modes within a single text, event, or practice. It investigates the specific resources and conventions of different modes (such as language, image, sound, gesture, and space) and analyzes how they are integrated, organised, and deployed to achieve communicative goals, thereby offering a comprehensive framework for analyzing complex communication across diverse social and cultural contexts.

Ontological type

Theoretical Frameworks

Analytical Methods

Digital Media Technologies

Key developments

Key Figures

Integrated Fusion and Open Tools era

Bernd J. Pichler[1] is a pivotal figure in multimodality during the Integrated Fusion and Open Tools era (2000–2012), with affiliations linked to TH Bingen University of Applied Sciences[2] and the University of California, Davis[3]. His key contributions include advancing clinically oriented multimodal workflows and promoting open, standardized PET/MRI approaches, as exemplified by the 2010 paper PET/MRI: Paving the Way for the Next Generation of Clinical Multimodality Imaging Applications[4], which highlighted end-to-end fusion pipelines as essential for translational impact. This era's emphasis on cross-institutional collaboration is echoed in Pichler[1]'s leadership across TH Bingen University of Applied Sciences[2] and the University of California, Davis[3], helping to scaffold durable guidelines for cross-modality reporting. By anchoring discussions around standardized acquisition protocols and reproducible fusion workflows, Pichler[1] contributed lasting principles for preserving cross-modality information in integrated imaging campaigns, as illustrated by the PET/MRI paper[4].

Data-Driven Fusion Maturation era

Luigi P. Badano [1] is a prominent imaging scientist whose work in this era connected University of Chicago [3] and KU Leuven [4]. His key contributions include guiding the Expert Consensus for Multimodality Imaging Evaluation of Adult Patients during and after Cancer Therapy: A Report from the American Society of Echocardiography and the European Association of Cardiovascular Imaging [7], underlining standardized multimodality imaging workflows and enabling reproducible cross-modal inference. Thomas H. Marwick [2] is a recognized leader whose work in this era connects Cornell University [5] and University of Toronto [6]. He co-authored the same Expert Consensus paper [7], shaping cross-modality diagnostic pathways for cancer therapy monitoring and enabling comprehensive phenotyping across imaging modalities, a milestone for data-driven fusion maturation.

Cross-Modal Deep Learning era

Muhammad Attique Khan[1] is a researcher at Stanford University[3] and Shandong University[4] during the Cross-Modal Deep Learning era (2020-2024). In the 2020 paper[7], Khan[1] contributed to multimodal brain tumor classification by integrating deep learning with robust feature selection, a foundational step for uncertainty-aware and transferable cross-modal representations in radiology. Roberto Damaševičius[2] is affiliated with Medical University of Vienna[5] and the University of Hong Kong[6] during this era. In the same 2020 paper[7], Damaševičius[2] contributed to advancing cross-modal brain tumor classification through integrative modeling and robust feature strategies, underscoring the importance of scalable cross-modal pipelines for biomarker discovery across imaging and clinical data.