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Application of Deep Learning in Medical Image Analysis (DL-MIA)

Submission Deadline: 30 September 2022 (closed)

Guest Editors

Prof. Kelvin KL Wong, The University of Adelaide, Australia
Prof. Simon Fong, University of Macau, Macau Special Administrative Region of China
Prof. Dhanjoo Ghista, University 2020 Foundation, USA


In recent years, artificial intelligence has developed rapidly in the medical field, which is largely due to the development and progress of deep learning (DL) technology. Due to the rapid advancement of computer technology, medical imaging applications based on DL have become a new engine of innovation in the medical field. In the field of medical imaging, machine learning based on DL has a positive impact on the optimization of image reconstruction, lesion segmentation, computer-aided detection and computer-aided diagnosis. Deep learning (DL) combined with medical image analysis (MIA) can provide what is known as DL-MIA. Artificial intelligence has been applied to magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, X-ray, mammography, color gastroscopy, multi-modal imaging and so on. At the same time, DL-MIA has applications in different medical fields, such as neurology, ophthalmology, cardiology, geriatrics, etc. At present, the commonly used DL technologies include convolutional neural network (CNN), recurrent neural network (RNN), and deep generation network (such as GAN, SAE, DBM, DBN). In the next few decades, DL-MIA technology may have a positive impact on the progress of medical image analysis, but there are still some challenges to be solved.

The purpose of this special issue is to collect the latest research results of key issues and topics related to medical images, and has the characteristics of flexibility, consistency, extensibility and universality. They can be regarding the classification, detection, and segmentation of medical images. We suggest that the authors provide as many research details as possible, a detailed research paper or a comprehensive review.

Papers are invited from the following suggested topics but not limited to:

· Detection classification and lesion staging using DL-MIA

· Artificial intelligence applied to image detection (including anatomical location and lesion location)

· Medical image reconstruction based on DL-MIA

· Medical image registration based on DL-MIA

· Medical image segmentation based on DL-MIA

· Disease classification based on MRI and CT scans 

Published Papers

  • Open Access


    BMRMIA: A Platform for Radiologists to Systematically Learn Automated Medical Image Analysis by Three Dimensional Medical Decision Support System

    Yankun Cao, Lina Xu, Zhi Liu, Xiaoyan Xiao, Mingyu Wang, Qin Li, Hongji Xu, Geng Yang
    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 851-863, 2022, DOI:10.32604/cmes.2022.018424
    (This article belongs to the Special Issue: Application of Deep Learning in Medical Image Analysis (DL-MIA))
    Abstract Contribution: This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology. The platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system, and strengthen the teaching practice of deep learning related courses in hospitals, so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary diagnosis. Background: In recent years, deep learning has been widely used in academia, industry, and medicine. An increasing number of companies are starting to recruit a large number… More >

  • Open Access


    Modelling Design of Color Graphics Books Using Visual Vocabulary Based on Children’s Color Language Preferences

    Wanni Xu, Huasen Xu, Xingyu Guo
    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 1171-1192, 2022, DOI:10.32604/cmes.2022.017824
    (This article belongs to the Special Issue: Application of Deep Learning in Medical Image Analysis (DL-MIA))
    Abstract Color language has important meaning for children’s picture books. Understanding children’s preferences in terms of color language will be helpful when designers model computerized picture books in order to adapt to children’s visual senses and effectively stimulate children’s interest in reading. In this study, we aimed to further explore the general characteristics of children’s preference for colors, color depth and color matching forms in picture books. For the study, 256 children between the ages of three and six were selected and divided into four groups. According to the implementation needs of the children’s color preference… More >

  • Open Access


    Breast Tumor Computer-Aided Detection System Based on Magnetic Resonance Imaging Using Convolutional Neural Network

    Jing Lu, Yan Wu, Mingyan Hu, Yao Xiong, Yapeng Zhou, Ziliang Zhao, Liutong Shang
    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 365-377, 2022, DOI:10.32604/cmes.2021.017897
    (This article belongs to the Special Issue: Application of Deep Learning in Medical Image Analysis (DL-MIA))
    Abstract Background: The main cause of breast cancer is the deterioration of malignant tumor cells in breast tissue. Early diagnosis of tumors has become the most effective way to prevent breast cancer. Method: For distinguishing between tumor and non-tumor in MRI, a new type of computer-aided detection CAD system for breast tumors is designed in this paper. The CAD system was constructed using three networks, namely, the VGG16, Inception V3, and ResNet50. Then, the influence of the convolutional neural network second migration on the experimental results was further explored in the VGG16 system. Result: CAD system built based… More >

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