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Electronics
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Machine Learning for Biomedical Imaging Applications
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Machine Learning for Biomedical Imaging Applications

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

Deadline for manuscript submissions: closed (15 June 2024) | Viewed by 5803

Special Issue Editor

Prof. Dr. Jyh-Cheng Chen

E-Mail Website
Guest Editor
Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
Interests: AI in biomedical imaging; deep learning; radiomics; medical imaging physics; PET/SPECT/CT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning (ML) has recently become a very popular buzzword as a consequence of disruptive technical advances and impressive experimental results, notably in the field of biomedical imaging. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of ML to clinical applications. With ML becoming a more mainstream tool for typical biomedical image analysis, such as diagnosis, segmentation, or classification, the key to safe and effective use of clinical artificial intelligence (AI) applications depends on both clinicians and AL researchers.

This Special Issue aims to discuss practical applications of machine learning technologies in biomedical imaging and to enable the next generation of strong AI methods, ensuring robust and interpretable AI-based solutions. We hope that clinicians can better understand and effectively use this emerging technology, and AI researchers can further improve models and algorithms from clinical feedback.

To this end, we invite articles that bridge the gap between machine learning research and its biomedical imaging applications to be submitted and published.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Disease detection, disease classification, disease characterization, and disease screening;
  • Treatment outcome prediction, treatment response evaluation;
  • Image quality improvement, image acquisition acceleration;
  • Radiation dose reduction, synthetic image generation across different modalities.

We look forward to receiving your contributions.

Prof. Dr. Jyh-Cheng Chen
Guest Editor

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
  • artificial intelligence
  • biomedical imaging

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Published Papers (2 papers)

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12 pages, 1237 KiB  
Article
Neural Network Helps Determine the Hemorrhagic Risk of Cerebral Arteriovenous Malformation
by Kuan-Yu Wang and Jyh-Cheng Chen
Electronics 2023, 12(20), 4241; https://doi.org/10.3390/electronics12204241 - 13 Oct 2023
Cited by 2 | Viewed by 1467
Abstract
We aimed to determine whether the hemorrhage risks of cerebral arteriovenous malformation (AVM), evaluated through digital subtraction angiography (DSA) using a neural network, were superior to those assessed through angioarchitecture. We conducted a retrospective review of patients with cerebral AVM who underwent DSA [...] Read more.
We aimed to determine whether the hemorrhage risks of cerebral arteriovenous malformation (AVM), evaluated through digital subtraction angiography (DSA) using a neural network, were superior to those assessed through angioarchitecture. We conducted a retrospective review of patients with cerebral AVM who underwent DSA from 2011 to 2017. Angioarchitecture parameters, age, and sex were analyzed using univariate and multivariate logistic regression. Additionally, a neural network was trained using a combination of convolutional neural network (CNN) and recurrent neural network (RNN) architectures. The training dataset consisted of 118 samples, while 29 samples were reserved for testing. After adjusting for age at diagnosis and sex, single venous drainage (odds ratio [OR] = 2.48, p = 0.017), exclusive deep venous drainage (OR = 3.19, p = 0.005), and venous sac (OR = 0.43, p = 0.044) were identified as independent risk factors for hemorrhage. The angioarchitecture-based hemorrhagic prediction model achieved 69% accuracy with an AUC (area under the ROC curve) of 0.757, while the CNN–RNN-based model achieved 76% accuracy with an AUC of 0.748. We present a diagnostic performance for hemorrhagic risk assessment of AVMs that is comparable to the angioarchitectural analysis. By leveraging larger datasets, there is significant potential to enhance prediction accuracy further. The CNN–RNN algorithm not only can potentially streamline workflow within the angio-suite but also serves as a complementary approach to optimize diagnostic accuracy and treatment strategies. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Imaging Applications)
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51 pages, 1540 KiB  
Systematic Review
Computer-Aided Diagnosis Systems for Automatic Malaria Parasite Detection and Classification: A Systematic Review
by Flavia Grignaffini, Patrizio Simeoni, Anna Alisi and Fabrizio Frezza
Electronics 2024, 13(16), 3174; https://doi.org/10.3390/electronics13163174 - 11 Aug 2024
Cited by 7 | Viewed by 3390
Abstract
Malaria is a disease that affects millions of people worldwide with a consistent mortality rate. The light microscope examination is the gold standard for detecting infection by malaria parasites. Still, it is limited by long timescales and requires a high level of expertise [...] Read more.
Malaria is a disease that affects millions of people worldwide with a consistent mortality rate. The light microscope examination is the gold standard for detecting infection by malaria parasites. Still, it is limited by long timescales and requires a high level of expertise from pathologists. Early diagnosis of this disease is necessary to achieve timely and effective treatment, which avoids tragic consequences, thus leading to the development of computer-aided diagnosis systems based on artificial intelligence (AI) for the detection and classification of blood cells infected with the malaria parasite in blood smear images. Such systems involve an articulated pipeline, culminating in the use of machine learning and deep learning approaches, the main branches of AI. Here, we present a systematic literature review of recent research on the use of automated algorithms to identify and classify malaria parasites in blood smear images. Based on the PRISMA 2020 criteria, a search was conducted using several electronic databases including PubMed, Scopus, and arXiv by applying inclusion/exclusion filters. From the 606 initial records identified, 135 eligible studies were selected and analyzed. Many promising results were achieved, and some mobile and web applications were developed to address resource and expertise limitations in developing countries. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Imaging Applications)
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