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

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.