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Bioengineering
Special Issues
Artificial Intelligence in Advanced Medical Imaging - 2nd Edition
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Artificial Intelligence in Advanced Medical Imaging - 2nd Edition

A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 3866

Special Issue Editors

Dr. Qingliang Jiao

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Guest Editor
School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
Interests: deep learning; image fusion for medical imaging; MRI image enhancement; transformer
Special Issues, Collections and Topics in MDPI journals
Dr. Ming Liu

E-Mail Website
Guest Editor
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
Interests: artificial intelligence; MRI image denoising; computational imaging
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Liquan Dong

E-Mail Website
Guest Editor
School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
Interests: deep learning; computational imaging technique; noninvasive measurement of physiological parameters
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical imaging technology has been widely used to generate anatomically precise images of tissue in vivo. However, the quality of medical images can be degraded severely by many existing factors during the acquisition procedure, including stochastic variation, numerous physiological processes, eddy currents, artifacts of magnetic susceptibilities between neighboring tissues, rigid body motion, and nonrigid motion. Recently, with the development of artificial intelligence technology, the combination of advanced technologies in these two fields has begun to provide important clinical information and play an important role in disease diagnosis, staging, treatment, and surgical planning.

Therefore, this upcoming Special Issue of Bioengineering, entitled “Artificial Intelligence in Advanced Medical Imaging”, will focus on original research papers and comprehensive reviews involving the processing of computational biomedical image, the information fusion of multimodality medical bioimages, quality evaluation, and biomedical image improvements. Topics of interest for this Special Issue include, but are not limited to, the following areas:

  1. Advanced artificial intelligence-based computational imaging techniques for biomedical imaging, involving the application of few-/zero-shot learning and self-supervised learning for biomedical imaging;
  2. Advanced medical image processing technology based on deep learning, such as denoising, reconstruction, and enhancement;
  3. Accurate assessment methods for biological image quality, including full reference or no reference;
  4. Novel multimodality medical biomedical image fusion method based on artificial intelligence.

Dr. Qingliang Jiao
Dr. Ming Liu
Prof. Dr. Liquan Dong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (2 papers)

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Research

21 pages, 3240 KiB  
Article
Deep Transfer Learning Using Real-World Image Features for Medical Image Classification, with a Case Study on Pneumonia X-ray Images
by Chanhoe Gu and Minhyeok Lee
Bioengineering 2024, 11(4), 406; https://doi.org/10.3390/bioengineering11040406 - 20 Apr 2024
Cited by 3 | Viewed by 2004
Abstract
Deep learning has profoundly influenced various domains, particularly medical image analysis. Traditional transfer learning approaches in this field rely on models pretrained on domain-specific medical datasets, which limits their generalizability and accessibility. In this study, we propose a novel framework called real-world feature [...] Read more.
Deep learning has profoundly influenced various domains, particularly medical image analysis. Traditional transfer learning approaches in this field rely on models pretrained on domain-specific medical datasets, which limits their generalizability and accessibility. In this study, we propose a novel framework called real-world feature transfer learning, which utilizes backbone models initially trained on large-scale general-purpose datasets such as ImageNet. We evaluate the effectiveness and robustness of this approach compared to models trained from scratch, focusing on the task of classifying pneumonia in X-ray images. Our experiments, which included converting grayscale images to RGB format, demonstrate that real-world-feature transfer learning consistently outperforms conventional training approaches across various performance metrics. This advancement has the potential to accelerate deep learning applications in medical imaging by leveraging the rich feature representations learned from general-purpose pretrained models. The proposed methodology overcomes the limitations of domain-specific pretrained models, thereby enabling accelerated innovation in medical diagnostics and healthcare. From a mathematical perspective, we formalize the concept of real-world feature transfer learning and provide a rigorous mathematical formulation of the problem. Our experimental results provide empirical evidence supporting the effectiveness of this approach, laying the foundation for further theoretical analysis and exploration. This work contributes to the broader understanding of feature transferability across domains and has significant implications for the development of accurate and efficient models for medical image analysis, even in resource-constrained settings. Full article
(This article belongs to the Special Issue Artificial Intelligence in Advanced Medical Imaging - 2nd Edition)
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13 pages, 2897 KiB  
Article
Prediction of Impulsive Aggression Based on Video Images
by Borui Zhang, Liquan Dong, Lingqin Kong, Ming Liu, Yuejin Zhao, Mei Hui and Xuhong Chu
Bioengineering 2023, 10(8), 942; https://doi.org/10.3390/bioengineering10080942 - 8 Aug 2023
Viewed by 1314
Abstract
In response to the subjectivity, low accuracy, and high concealment of existing attack behavior prediction methods, a video-based impulsive aggression prediction method that integrates physiological parameters and facial expression information is proposed. This method uses imaging equipment to capture video and facial expression [...] Read more.
In response to the subjectivity, low accuracy, and high concealment of existing attack behavior prediction methods, a video-based impulsive aggression prediction method that integrates physiological parameters and facial expression information is proposed. This method uses imaging equipment to capture video and facial expression information containing the subject’s face and uses imaging photoplethysmography (IPPG) technology to obtain the subject’s heart rate variability parameters. Meanwhile, the ResNet-34 expression recognition model was constructed to obtain the subject’s facial expression information. Based on the random forest classification model, the physiological parameters and facial expression information obtained are used to predict individual impulsive aggression. Finally, an impulsive aggression induction experiment was designed to verify the method. The experimental results show that the accuracy of this method for predicting the presence or absence of impulsive aggression was 89.39%. This method proves the feasibility of applying physiological parameters and facial expression information to predict impulsive aggression. This article has important theoretical and practical value for exploring new impulsive aggression prediction methods. It also has significance in safety monitoring in special and public places such as prisons and rehabilitation centers. Full article
(This article belongs to the Special Issue Artificial Intelligence in Advanced Medical Imaging - 2nd Edition)
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