posted by user: zhangyudong || 2756 views || tracked by 6 users: [display]

DLMMIA 2019 : Deep Learning Methods for Medical Image Analysis in INDIN 2019

FacebookTwitterLinkedInGoogle

Link: http://indin2019.org/special-sessions/ss01-deep-learning
 
When Jul 23, 2019 - Jul 25, 2019
Where Helsinki-Espoo, Finland
Submission Deadline Feb 15, 2019
Notification Due Apr 22, 2019
Final Version Due Jun 5, 2019
Categories    biomedical image analysis   deep learning   artificial intelligence
 

Call For Papers

Special Session Organized by
Yu-Dong Zhang, University of Leicester, United Kingdom;
Shui-Hua Wang, University of Loughborough, United Kingdom;

With advancement in biomedical imaging, the amount of data generated are increasing in biomedical engineering. For example, data can be generated by multimodality image techniques, e.g. ranging from Computed Tomography (CT), Magnetic Resonance Imaging (MR), Ultrasound, Single Photon Emission Computed Tomography (SPECT), and Positron Emission Tomography (PET), to Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy, etc. This poses a great challenge on how to develop new advanced imaging methods and computational models for efficient data processing, analysis and modelling in clinical applications and in understanding the underlying biological process.

Deep learning is a rapidly advancing field in recent years, in terms of both methodological development and practical applications. It allows computational models of multiple processing layers to learn and represent data with multiple levels of abstraction. It is able to implicitly capture intricate structures of largescale data and ideally suited to some of the hardware architectures that are currently available.

The focus of this special session is to carry out the research article which could be more focused on to the latest medical image analysis techniques based on Deep learning. In recent years Deep Learning method and its variants has been widely used by researchers. This Issue intends to bring new DL algorithm with some Innovative Ideas and find out the core problems in medical image analysis.

Topics under this track include (but not limited to):
Application 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
New Model of New Structure of convolutional neural network
Visualization and Explainable deep neural network

Authors intending to publish their results in IEEE Transactions on Industrial Informatics (I.F.=5.430) should consider the fast track to transactions opportunity.

Related Resources

CMES_RADLMSA 2020   CMES_Recent Advances on Deep Learning for Medical Signal Analysis (IF: 0.796)
ECAI 2020   24th European Conference on Artificial Intelligence
ADLMBIA 2020   Advanced Deep Learning Methods for Biomedical Information Analysis (IF: 2.031)
ICML 2020   37th International Conference on Machine Learning
IEEE AIML4COINS 2020   IEEE AIML4COINS2020 | Artificial Intelligence | Machine Learning | Deep Learning | Machine Vision | Big Data Analytics | Video Analytics | Speech Recognition | NLP
IWUAS 2020   2020 International Workshop on Unmanned Aircraft Systems (IWUAS 2020)
CVPR 2020   Computer Vision and Pattern Recognition
SSPD 2020   Sensor Signal Processing for Defence Conference 2020
ISBDAI 2020   【Ei Compendex Scopus】2018 International Symposium on Big Data and Artificial Intelligence
SATRANH 2020   Special Issue of APPLIED SCIENCES on Static Analysis Techniques: Recent Advances and New Horizons