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IEEE TCSVT Special Issue 2021 : Learning with Multimodal Data for Biomedical Informatics

Event Date: Jul 22, 2020 - Jun 30, 2021
Submission Deadline: Oct 15, 2020
Notification of Acceptance: Dec 15, 2020
Camera Ready Version Due : Mar 30, 2021


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.

Call For Paper

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.

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