[visionlist] IEEE TCSVT Special Issue on Learning with Multimodal Data for Biomedical Informatics

Yao, Bing bing.yao at okstate.edu
Wed Jul 22 20:20:00 -04 2020

Dear Colleagues,

We would like to invite paper submissions to the IEEE TCSVT special issue on “Learning with Multimodal Data for Biomedical Informatics”.

Call for Papers

IEEE Transactions on Circuits and Systems for Video Technology

Special Issue on

Learning with Multimodal Data for Biomedical Informatics

Fast-growing biomedical and healthcare data of multiple modalities have encompassed multiple scales ranging from molecules, individuals, to populations. Meanwhile, the heterogeneous and increasingly more diverse modalities of the data present major barriers toward their understanding, fusion, and translation into effective clinical actions. For example, electronic health records (EHRs) are representative examples of multimodal/multisource data collections; including not only traditional medical measurements, but also images, videos, audios, and free texts. Other examples include mobile health for remote patient care with typical data modalities such as patient- or caregiver-generated photos, self-reported symptoms of pain, and body temperature. The diversity of such information sources and the increasing amounts of medical data produced by healthcare institutes annually, pose significant challenges for data-driven biomedical analysis. While biomedical and healthcare research traditionally focuses on the structured measurement data, the growing availability of novel data modalities has created a compelling demand for novel machine learning, image/video/audio/text processing, and multi-modal fusion algorithms that specifically tackle the unique challenges associated with biomedical and healthcare data and allow decision-makers and stakeholders to better interpret and exploit the data. This special issue aims at catalyzing synergies among image/video processing, text/speech understanding, machine learning, multi-modal learning and other related fields with the goals to (1) develop novel data-driven models to accelerate knowledge discovery in biomedicine through the seamless integration of medical data collected from imaging systems, laboratory and wearable devices, as well as other related medical devices; (2) promote the development of new multi-modal learning systems to enhance the healthcare quality and patient safety; and (3) promote new applications in biomedical informatics that can leverage or benefits from the integration of multi-modal data and machine learning.


We welcome high-quality submissions with important new theories, methods, applications, and insights at the intersection of image/video processing, text/speech understanding, machine learning, multi-modal learning, and biomedical informatics. The topics of interest include, but are not limited to:

  *   Developing and applying cutting-edge machine and multi-modal learning techniques to tackle real-world medical and healthcare problems.
  *   Developing new machine learning approaches to improve the quantitative representation of high-dimensional medical images and videos for knowledge discovery in biomedicine
  *   Designing novel data-fusion methods to integrate multiple data sources and modalities for enhanced visualization, effective biomarker extraction, and optimal medical decision making.
  *   Addressing challenges and roadblocks in biomedical informatics with reference to the data-driven machine learning, such as imbalanced dataset, weakly-structured or unstructured data, noisy and ambiguous labeling, and more.
  *   Other closely related technical advances in image processing, video processing, audio processing, text understanding, and multi-modal fusion, with application potential in biomedical informatics.

The applications of interest may include: (1) Computational Biology, including the advanced interpretation of critical biological findings, using databases and cutting-edge computational infrastructure; (2) Clinical Informatics, including the scenarios of using computation and data for health care, spanning medicine, dentistry, nursing, pharmacy, and allied health; (3) Public Health Informatics, including the studies of patients and populations to improve the public health system and to elucidate epidemiology. (4) mobile Health Applications, including the use of mobile apps and wearable sensors for health management and wellness promotion; and (5) Cyber-Informatics Applications, including the use of social media data mining and natural language processing for clinical insight discovery and medical decision making.


  *   Paper Submission: October 15, 2020
  *   First Notification: December 15, 2020
  *   Revised Manuscript: January 15, 2020
  *   Notification of Acceptance: February 15, 2021
  *   Final Manuscript Due: March 30, 2021
  *   Tentative Publication Date: June 30, 2020


  *   Zhangyang (Atlas) Wang, The University of Texas at Austin
  *   Vishal Patel, Johns Hopkins University
  *   Bing Yao, Oklahoma State University
  *   Steve Jiang, University of Texas Southwestern Medical Center
  *   Huimin Lu, Kyushu Institute of Technology, Japan
  *   Yang Shen, Texas A&M University


  *   Read the Information for Authors at https://ieee-cas.org/pubs/tcsvt/information-authors
  *   Submit your manuscript at the TCSVT webpage (https://mc.manuscriptcentral.com/tcsvt) and follow the submission procedure. At Submission Step 1 (i.e., Type, Title, & Abstract), please select “Transactions Papers – Special Issue on Learning with Multimodal Data for Biomedical Informatics” as your manuscript type. Additionally, please clearly indicate in the cover letter that the manuscript is submitted to this special issue.
  *   Early submissions are welcome. We will start the review process as soon as we receive your valuable contributions.

Bing Yao, Ph.D.

Assistant Professor

School of Industrial Engineering & Management

Oklahoma State University

342 Engineering North, Stillwater, OK, 74078

Email: bing.yao at okstate.edu<mailto:bing.yao at okstate.edu>; Tel: 405-744-6055

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