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ACISCMI 2018 : Advanced Computational Intelligence and Soft Computing in Medical Imaging (Journal of Medical Systems, IF: 2.456)


When N/A
Where N/A
Abstract Registration Due May 1, 2018
Submission Deadline Jul 1, 2018
Notification Due Aug 1, 2018
Final Version Due Sep 1, 2018
Categories    biomedical image analysis   machine learning   deep learning   computational intelligence

Call For Papers

With advancement in biomedical imaging, the amount of data 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.) has grown exponentially and the nature of such data is increasingly become more complex. 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.

The purpose of this topical collection aims to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of advanced imaging analysis in the multimodal biomedical imaging area. The ultimate goal is to promote research and development of advanced imaging analysis for multimodal biomedical images by publishing high-quality research articles and reviews in this rapidly growing interdisciplinary field.

The topics of interest include, but are not limited to:
 New algorithms, models and applications of advanced imaging methods
 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)
 Variational and combinatorial optimizations for biomedical imaging and image analysis
 Advanced Biomedical image analysis ( image processing, Statistical and probabilistic methods for biomedical imaging and image analysis, Machine learning in biomedical imaging and image analysis)
 Deep learning methods (convolutional neural network, autoencoder, deep belief network, etc.)
 Visualization

Please make sure to select the “Advanced CI and SC in Medical Imaging” article type during the submission process.
Any queries related to this topical collection should be addressed contact corresponding editor: Dr. Yudong Zhang (

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