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Electronics
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Deep Learning and Big Data Applications in Medical Image Analysis
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Deep Learning and Big Data Applications in Medical Image Analysis

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 5218

Special Issue Editors

Prof. Dr. Wei Qian

E-Mail Website
Guest Editor
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China
Interests: different modality medical imaging; radiomics; big data analysis
Dr. Jiangli Lin

E-Mail Website
Guest Editor
School of Biomedical Engineering, Sichuan University, Chengdu 610065, China
Interests: medical imaging; intelligent diagnosis of medical images; image processing and pattern recognition; biological tissue viscoelasticity detection
Dr. Jianhua Wang

E-Mail Website
Guest Editor
Department of Radiology, The Affiliated Hospital of Medicine School of Ningbo University, Ningbo 315000, China
Interests: tumor intelligence; molecular imaging

Special Issue Information

Dear Colleagues,

This Special Issue, "Deep Learning and Big Data Applications in Medical Image Analysis", focuses on challenges concerning AI applications in medical and clinical settings. Topics of interest include, but are not limited to:

  • Computer-aided detection, diagnosis and early disease prediction;
  • Medical imaging, multimodality medical imaging and radiomics;
  • A structured image reporting system based on artificial intelligence;
  • Three-dimensional reconstruction of medical images;
  • Wearable medical devices;
  • Medical robot;
  • Surgical navigation;
  • The ethics of medical artificial intelligence;
  • The latest technological development of artificial intelligence and its application in medical imaging and medical clinic;
  • Medical image registration;
  • Image interpretation;
  • Medical image information fusion;
  • Data analysis and synthesis.

Prof. Dr. Wei Qian
Dr. Jiangli Lin
Dr. Jianhua Wang
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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.

Keywords

  • medical image
  • medical clinic
  • data analysis
  • artificial intelligence

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Published Papers (2 papers)

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Research

21 pages, 5556 KiB  
Article
Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing Homes
by Feng Zhou, Shijing Hu, Xiaoli Wan, Zhihui Lu and Jie Wu
Electronics 2023, 12(12), 2581; https://doi.org/10.3390/electronics12122581 - 7 Jun 2023
Cited by 11 | Viewed by 2536
Abstract
In the context of population aging, to reduce the run on public medical resources, nursing homes need to predict the health risks of the elderly periodically. However, there is no professional medical testing equipment in nursing homes. In the current disease risk prediction [...] Read more.
In the context of population aging, to reduce the run on public medical resources, nursing homes need to predict the health risks of the elderly periodically. However, there is no professional medical testing equipment in nursing homes. In the current disease risk prediction research, many datasets are collected by professional medical equipment. In addition, the currently researched models cannot be run directly on mobile terminals. In order to predict the health risks of the elderly without relying on professional medical testing equipment in the application scenarios of nursing homes, we use the datasets collected by non-professional medical testing equipment. Based on transfer learning and lightweight neural networks, we propose a disease risk prediction model, Diplin (disease risk prediction model based on lightweight neural network), applied to nursing homes. This model achieved 98% accuracy, 97% precision, 96% recall, 95% specificity, 97% F1 score, and 1.0 AUC (area under ROC curve) value on the validation set. The experimental results show that in the application scenario of nursing homes, the Diplin model can provide practical support for predicting the health risks of the elderly, and this model can be run directly on the tablet. Full article
(This article belongs to the Special Issue Deep Learning and Big Data Applications in Medical Image Analysis)
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12 pages, 1103 KiB  
Article
A Multi-Task Dense Network with Self-Supervised Learning for Retinal Vessel Segmentation
by Zhonghao Tu, Qian Zhou, Hua Zou and Xuedong Zhang
Electronics 2022, 11(21), 3538; https://doi.org/10.3390/electronics11213538 - 30 Oct 2022
Cited by 3 | Viewed by 2130
Abstract
Morphological and functional changes in retinal vessels are indicators of a variety of chronic diseases, such as diabetes, stroke, and hypertension. However, without a large number of high-quality annotations, existing deep learning-based medical image segmentation approaches may degrade their performance dramatically on the [...] Read more.
Morphological and functional changes in retinal vessels are indicators of a variety of chronic diseases, such as diabetes, stroke, and hypertension. However, without a large number of high-quality annotations, existing deep learning-based medical image segmentation approaches may degrade their performance dramatically on the retinal vessel segmentation task. To reduce the demand of high-quality annotations and make full use of massive unlabeled data, we propose a self-supervised multi-task strategy to extract curvilinear vessel features for the retinal vessel segmentation task. Specifically, we use a dense network to extract more vessel features across different layers/slices, which is elaborately designed for hardware to train and test efficiently. Then, we combine three general pre-training tasks (i.e., intensity transformation, random pixel filling, in-painting and out-painting) in an aggregated way to learn rich hierarchical representations of curvilinear retinal vessel structures. Furthermore, a vector classification task module is introduced as another pre-training task to obtain more spatial features. Finally, to make the segmentation network pay more attention to curvilinear structures, a novel dynamic loss is proposed to learn robust vessel details from unlabeled fundus images. These four pre-training tasks greatly reduce the reliance on labeled data. Moreover, our network can learn the retinal vessel features effectively in the pre-training process, which leads to better performance in the target multi-modal segmentation task. Experimental results show that our method provides a promising direction for the retinal vessel segmentation task. Compared with other state-of-the-art supervised deep learning-based methods applied, our method requires less labeled data and achieves comparable segmentation accuracy. For instance, we match the accuracy of the traditional supervised learning methods on DRIVE and Vampire datasets without needing any labeled ground truth image. With elaborately training, we gain the 0.96 accuracy on DRIVE dataset. Full article
(This article belongs to the Special Issue Deep Learning and Big Data Applications in Medical Image Analysis)
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