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A special issue for AIHC-Springer
Journal of Ambient Intelligence and Humanized Computing
Editor-in-Chief: Vincenzo Loia
ISSN: 1868-5137 (print version), ISSN: 1868-5145 (electronic version)
https://www.springer.com/engineering/computational+intelligence+and+complexity/journal/12652
Special Issue on
“Deep Learning Methods for Biomedical Information Analysis”
==Overviews==
Due to numerous biomedical information sensing devices, such as, Computed Tomography (CT), Magnetic Resonance (MR) Imaging, 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. Large amount of biomedical information was gathered these years. However, how to develop new advanced imaging methods and computational models for efficient data processing, analysis and modelling from the colleccted data is important for clinical applications and in understanding the underlying biological process.
Deep learning has been rapidly developed recent years, in terms of both methodological development and practical applications. It provides 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 large-scale data and ideally suited to some of the hardware architectures that are currently available.
The purpose of this special issue aims to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of Deep learning and Computational Machine Learning, to solve 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 and reviews in this rapidly growing interdisciplinary field.
Scopes (but are not limited to) the following:
• 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
==Submission Instructions==
Before submission authors should carefully read over the Instructions for Authors, which are located at https://www.springer.com/engineering/computational+intelligence+and+complexity/journal/12652.
Prospective authors should submit an electronic copy of their complete manuscript through the Springer submission system at https://www.editorialmanager.com/aihc/default.aspx according to the submission schedule. Please select the special issue “Deep Learning Methods for Biomedical Information Analysis” for your submission. All submissions will undergo initial screening.
==Important dates==
• Submission deadline: June 1, 2019,
• Review notification: Nov 30, 2019
• Final decision: Dec 30, 2019
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