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Electronics
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Deep Learning in Medical Image Process
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Deep Learning in Medical Image Process

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 4837

Special Issue Editors

Prof. Dr. Che-Lun Hung

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Guest Editor
Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan
Interests: smart medicine; deep learning; medical image analysis; bioinformatics computing; high-performance computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chun-Yuan Lin

E-Mail Website
Guest Editor
Department of Information Engineering and Computer Science, Asia University, Taichung 41354, Taiwan
Interests: parallel processing (languages, algorithms); bioinformatics (genomics, proteomics, systems biology, next-generation sequencing); cheminformatics (drug design, computational chemistry); neurocomputing (drosophila brain research); machine learning (deep learning, extreme learning machine, SVM, neural networks)
Prof. Dr. Frédéric Magoulès

E-Mail Website
Guest Editor
Department of Applied Mathematics & Computer Science, Universite Paris-Saclay, Saint-Aubin, Paris, France
Interests: cloud computing; numerical analysis; high-performance computing

Special Issue Information

Dear Colleagues,

Medical imaging techniques, such as X-rays, magnetic resonance imaging (MRIs), computed tomography scans (CT), ultrasound, and so forth, are used to create images of various parts of the human body for the medical practitioner and doctors to evaluate and analyze abnormality in the images. Computer-aided detection or computer-aided diagnosis is the computer-based system for better interpreting medical images, which has been a longstanding issue in the medical imaging field.

Recently, deep learning has shown its power to successfully help to identify, classify, segment, reconstruct, and quantify patterns in images. Many deep learning models have been applied to identify abnormality and highlight conspicuous parts in medical images. Meanwhile, deep learning models are used for reconstruction, denoising, and enhancement quality of medical images.

Deep learning technologies are the power tools that facilitate physicians in diagnosing diseases through medical images in clinical environments. The purpose of this Special Issue “Deep Learning in Medical Image Process” is to present and highlight novel algorithms, architectures, techniques, and applications of deep learning for medical image processes.

Prof. Dr. Che-Lun Hung
Prof. Dr. Chun-Yuan Lin
Prof. Dr. Frédéric Magoulès
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

  • machine learning
  • deep learning
  • transfer learning
  • medical image
  • computer-aided
  • explainable AI

Published Papers (2 papers)

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Research

18 pages, 5357 KiB  
Article
COVID-ResNet: COVID-19 Recognition Based on Improved Attention ResNet
by Tao Zhou, Xiaoyu Chang, Yuncan Liu, Xinyu Ye, Huiling Lu and Fuyuan Hu
Electronics 2023, 12(6), 1413; https://doi.org/10.3390/electronics12061413 - 16 Mar 2023
Cited by 2 | Viewed by 1429
Abstract
COVID-19 is the most widespread infectious disease in the world. There is an incubation period in the early stage of infection. At present, there are some difficulties in the diagnosis of COVID-19. Medical image analysis based on computed tomography (CT) images is an [...] Read more.
COVID-19 is the most widespread infectious disease in the world. There is an incubation period in the early stage of infection. At present, there are some difficulties in the diagnosis of COVID-19. Medical image analysis based on computed tomography (CT) images is an important tool for clinical diagnosis. However, the lesion size of COVID-19 is smaller, and the lesion shape of COVID-19 is more complex. The effect of the aided diagnosis model is not good. To solve this problem, an aided diagnostic model of COVID-ResNet was proposed based on CT images. Firstly, an improved attention ResNet model was designed based on CT images to focus on the focal lesion area. Secondly, the SE-Res block was constructed. The squeeze excitation mechanism with the residual connection was introduced into the ResNet. The SE-Res block can enhance the correlation degree among different channels and improve the overall accuracy of the model. Thirdly, MFCA (multi-layer feature converge attention) blocks were proposed, which extract multi-layer features. In this model, coordinated attention was used to focus on the direction information of the lesion area. Different layer features were concatenated so that the shallow layer and deep layer features were fused. The experimental results showed that the model could significantly improve the recognition accuracy of COVID-19. Compared with similar models, COVID-ResNet has better performance. On the COVID-19 CT dataset, the accuracy, recall rate, F1 score, and AUC value could reach 96.89%, 98.15%,96.96%, and 99.04%, respectively. Compared with the ResNet model, the accuracy, recall rate, F1 score, and AUC value were higher by 3.1%, 2.46%, 3.0%, and 1.16%, respectively. In ablation experiments, the experimental results showed that the SE-Res block and MFCA model proposed by us were effective. COVID-ResNet transfers the shallow features to the deep, gathers the features, and makes the information complementary. COVID-ResNet can improve the work efficiency of doctors and reduce the misdiagnosis rate. It has a positive significance for the computer-aided diagnosis of COVID-19. Full article
(This article belongs to the Special Issue Deep Learning in Medical Image Process)
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12 pages, 5690 KiB  
Article
Efficient Perineural Invasion Detection of Histopathological Images Using U-Net
by Youngjae Park, Jinhee Park and Gil-Jin Jang
Electronics 2022, 11(10), 1649; https://doi.org/10.3390/electronics11101649 - 22 May 2022
Cited by 2 | Viewed by 2381
Abstract
Perineural invasion (PNI), a sign of poor diagnosis and tumor metastasis, is common in a variety of malignant tumors. The infiltrating patterns and morphologies of tumors vary by organ and histological diversity, making PNI detection difficult in biopsy, which must be performed manually [...] Read more.
Perineural invasion (PNI), a sign of poor diagnosis and tumor metastasis, is common in a variety of malignant tumors. The infiltrating patterns and morphologies of tumors vary by organ and histological diversity, making PNI detection difficult in biopsy, which must be performed manually by pathologists. As the diameters of PNI nerves are measured on a millimeter scale, the PNI region is extremely small compared to the whole pathological image. In this study, an efficient deep learning-based method is proposed for detecting PNI regions in multiple types of cancers using only PNI annotations without detailed segmentation maps for each nerve and tumor cells obtained by pathologists. The key idea of the proposed method is to train the adopted deep learning model, U-Net, to capture the boundary regions where two features coexist. A boundary dilation method and a loss combination technique are proposed to improve the detection performance of PNI without requiring full segmentation maps. Experiments were conducted with various combinations of boundary dilation widths and loss functions. It is confirmed that the proposed method effectively improves PNI detection performance from 0.188 to 0.275. Additional experiments were also performed on normal nerve detection to validate the applicability of the proposed method to the general boundary detection tasks. The experimental results demonstrate that the proposed method is also effective for general tasks, and it improved nerve detection performance from 0.511 to 0.693. Full article
(This article belongs to the Special Issue Deep Learning in Medical Image Process)
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