posted by user: zhangyudong || 2293 views || tracked by 2 users: [display]

ADLMBIA 2020 : Advanced Deep Learning Methods for Biomedical Information Analysis (IF: 2.031)

FacebookTwitterLinkedInGoogle

Link: https://www.frontiersin.org/research-topics/9687/advanced-deep-learning-methods-for-biomedical-information-analysis-adlmbia
 
When N/A
Where N/A
Abstract Registration Due Nov 4, 2019
Submission Deadline Dec 1, 2019
Notification Due Feb 1, 2020
Final Version Due Mar 1, 2020
Categories    deep learning   trans fer learning   biomedical signal   information analysis
 

Call For Papers

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.

Related Resources

ICBCB--Ei 2026   2026 14th International Conference on Bioinformatics and Computational Biology (ICBCB 2026)
Ei/Scopus-CCNML 2025   2025 5th International Conference on Communications, Networking and Machine Learning (CCNML 2025)
Ei/Scopus-SGGEA 2025   2025 2nd Asia Conference on Smart Grid, Green Energy and Applications (SGGEA 2025)
CVAI 2026   2026 International Symposium on Computer Vision and Artificial Intelligence (CVAI 2026)
AAIML 2026   IEEE--2026 International Conference on Advances in Artificial Intelligence and Machine Learning
IEEE-ACAI 2025   2025 IEEE 8th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI 2025)
IEEE FMLDS 2025   2025 IEEE International Conference on Future Machine Learning and Data Science (FMLDS2025)
Ei/Scopus-MLBDM 2025   2025 5th International Conference on Machine Learning and Big Data Management (MLBDM 2025)
SOFEA 2025   11th International Conference on Software Engineering and Applications
EPSEE 2025   2025 4th International Conference on Advanced Electric Power System and Energy Engineering (EPSEE 2025)