Multimodal Image Processing

Multimodal Image Processing is a computational methodology that integrates and analyzes data from multiple imaging modalities to extract complementary information, distinguished by its focus on synergistic transformation, fusion, and interpretation of heterogeneous image sources for enhanced diagnostic or analytical outcomes.

432

Publications

30.3K

Citations

1.6K

Authors

638

Institutions

Publications per year

2017–2026

288

Authors

1.6K

  1. DS
    Dinggang Shen

    University of North Carolina at Chapel Hill, United States

    5 publications · 293 citations

    5

    h-index

  2. WL
    Weisheng Li

    Chongqing University of Posts and Telecommunications, China

    5 publications · 163 citations

    5

    h-index

  3. AM
    Anant Madabhushi

    Case Western Reserve University, United States

    4 publications · 85 citations

    4

    h-index

  4. JC
    Jonathan Chappelow

    Rutgers, The State University of New Jersey, United States

    4 publications · 85 citations

    4

    h-index

  5. DZ
    Dongming Zhou

    Yunnan University, China

    4 publications · 152 citations

    4

    h-index

1–5 of 1.6K

Venues

  1. Biomedical Signal Processing and Control

    15 publications · 755 citations

    15

    h-index

  2. 10

    h-index

  3. IEEE Access

    9 publications · 714 citations

    9

    h-index

  4. 9

    h-index

  5. Information Fusion

    8 publications · 499 citations

    8

    h-index

1–5