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Electronics
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Big Data and Machine Learning to Biomedical Applications
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Big Data and Machine Learning to Biomedical Applications

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

Deadline for manuscript submissions: 15 December 2024 | Viewed by 839

Special Issue Editor

Dr. Chenxi Huang

E-Mail Website
Guest Editor
School of Informatics, Xiamen University, Xiamen 361005, China
Interests: deep learning; artificial intelligence; multimedia analysis; medical image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the continuous development of technology, big data and machine learning technologies have shown tremendous potential in the field of biomedical science. These emerging technologies are helping us better understand the essence of diseases, accelerate the development of new drugs, improve medical services, increase clinical efficiency, and reduce medical accidents.

Although the application of big data and machine learning technologies in the biomedical field has shown unlimited potential, it also faces some challenges. Firstly, big data and machine learning technologies need to address data security and privacy issues. Medical data are sensitive information, and how to protect data security and privacy is an important issue facing the biomedical field. Secondly, big data and machine learning technologies need to address issues of credibility and accuracy. Human health is an important cornerstone of human development; therefore, patient data and treatment recommendations must be highly reliable and accurate, as they may otherwise cause serious risks and consequences.

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

  • Computer vision-based healthcare systems and applications;
  • Computer-aided diagnosis powered by computer vision or deep learning technology;
  • Innovative computer vision detection methods suitable for medical usage;
  • Survival prediction based on computer vision for diagnosis;
  • 3D reconstruction of human tissues, organs, and bodies for building medical models;
  • Emerging small-sample learning and/or meta learning in clinical imaging process and analysis;
  • Emerging small-sample learning and/or meta learning in medical signal processing.

I look forward to receiving your contributions.

Dr. Chenxi Huang
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

  • artificial intelligence
  • machine learning
  • deep learning
  • biomedical engineering
  • medical image processing
  • medical imaging
  • data analytics

Published Papers (1 paper)

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Research

18 pages, 6406 KiB  
Article
Contrastive Learning Joint Regularization for Pathological Image Classification with Noisy Labels
by Wenping Guo, Gang Han, Yaling Mo, Haibo Zhang, Jiangxiong Fang and Xiaoming Zhao
Electronics 2024, 13(13), 2456; https://doi.org/10.3390/electronics13132456 - 22 Jun 2024
Viewed by 521
Abstract
The annotation of pathological images often introduces label noise, which can lead to overfitting and notably degrade performance. Recent studies have attempted to address this by filtering samples based on the memorization effects of DNNs. However, these methods often require prior knowledge of [...] Read more.
The annotation of pathological images often introduces label noise, which can lead to overfitting and notably degrade performance. Recent studies have attempted to address this by filtering samples based on the memorization effects of DNNs. However, these methods often require prior knowledge of the noise rate or a small, clean validation subset, which is extremely difficult to obtain in real medical diagnosis processes. To reduce the effect of noisy labels, we propose a novel training strategy that enhances noise robustness without prior conditions. Specifically, our approach includes self-supervised regularization to encourage the model to focus more on the intrinsic connections between images rather than relying solely on labels. Additionally, we employ a historical prediction penalty module to ensure consistency between successive predictions, thereby slowing down the model’s shift from memorizing clean labels to memorizing noisy labels. Furthermore, we design an adaptive separation module to perform implicit sample selection and flip the labels of noisy samples identified by this module and mitigate the impact of noisy labels. Comprehensive evaluations of synthetic and real pathological datasets with varied noise levels confirm that our method outperforms state-of-the-art methods. Notably, our noise handling process does not require any prior conditions. Our method achieves highly competitive performance in low-noise scenarios which aligns with current pathological image noise situations, showcasing its potential for practical clinical applications. Full article
(This article belongs to the Special Issue Big Data and Machine Learning to Biomedical Applications)
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