Advanced Deep Learning Methods for Biomedical Information Analysis (IF: 2.031)
ADLMBIA 2020
- URL: https://www.frontiersin.org/research-topics/9687/advanced-deep-learning-methods-for-biomedical-information-analysis-adlmbia
- Event Date: ~
- Abstract Submission Date: 2019-11-04
- Submission Date: 2019-12-01
Deep learning approaches have been rapidly developed in recent years, both in terms of methodologies and practical applications. Deep learning techniques provide computational models of multiple processing layers to learn and represent data with multiple levels of abstraction. Deep Learning allows to implicitly capture intricate structures of large-scale data and ideally suited to some of the hardware architectures that are currently available.
The purpose of this Article Collection is to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of Deep learning and Computational Machine Learning, to solve problems in biomedical engineering. The ultimate goal is to promote research and development of deep learning for multimodal biomedical images by publishing high-quality research articles, reviews, or perspectives, among other article types, in this rapidly growing interdisciplinary field.
Topics include, but are not limited to:
- Theoretical understanding of deep learning in biomedical engineering
- Transfer learning and multi-task learning
- Joint Semantic Segmentation, Object Detection and Scene Recognition on biomedical images
- Improvising on the computation of a deep network, exploiting parallel computation techniques
and GPU programming
- Multimodal imaging techniques (data acquisition, reconstruction, 2D, 3D, 4D imaging, etc.)
- Translational multimodality imaging and biomedical applications (e.g., detection, diagnostic
analysis, quantitative measurements, image guidance of ultrasonography)
- Optimization by deep neural networks, Multi-dimensional deep learning
- New Model of New Structure of convolutional neural network
- Visualization and Explainable deep neural network
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
All accepted papers will be published on one of those journals: Frontiers in Big Data, Frontiers in Artificial Intelligence, Frontiers in Public Health (IF: 2.031), or Frontiers in Computer Science. The authors need to specify the journal during their submission procedure.
The purpose of this Article Collection is to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of Deep learning and Computational Machine Learning, to solve problems in biomedical engineering. The ultimate goal is to promote research and development of deep learning for multimodal biomedical images by publishing high-quality research articles, reviews, or perspectives, among other article types, in this rapidly growing interdisciplinary field.
Topics include, but are not limited to:
- Theoretical understanding of deep learning in biomedical engineering
- Transfer learning and multi-task learning
- Joint Semantic Segmentation, Object Detection and Scene Recognition on biomedical images
- Improvising on the computation of a deep network, exploiting parallel computation techniques
and GPU programming
- Multimodal imaging techniques (data acquisition, reconstruction, 2D, 3D, 4D imaging, etc.)
- Translational multimodality imaging and biomedical applications (e.g., detection, diagnostic
analysis, quantitative measurements, image guidance of ultrasonography)
- Optimization by deep neural networks, Multi-dimensional deep learning
- New Model of New Structure of convolutional neural network
- Visualization and Explainable deep neural network
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
All accepted papers will be published on one of those journals: Frontiers in Big Data, Frontiers in Artificial Intelligence, Frontiers in Public Health (IF: 2.031), or Frontiers in Computer Science. The authors need to specify the journal during their submission procedure.
Journal Promotion
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| Acta Koreana (Acta Koreana) |
|
| Acta Via Serica (AVS) |
|
| Computers and Concrete (CAC) |
|
| Journal of Information Processing Systems (JIPS) |
|
| Journal of Internet Computing and Services (KSII - JICS) |
|
| Journal of Smart Tourism (Smart Tourism) |
|
| Membrane and Water Treatment (MWT) |
|
| Wind and Structures, An International Journal (Wind and Structures) |
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