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Recent Advances in the Acquisition and Processing of Biomedical Signals and Images - 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 15 March 2027 | Viewed by 757

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Special Issue Information

Dear Colleagues,

Biomedical images and signals have the potential to provide information about the anatomical, structural, and functional properties of different organs and tissues, from single cells to the whole body. The development of new technologies in the field of biomedical and signal processing has led to a continuous evolution of both instrumentation and methods of analysis.

Furthermore, the development and application of advanced methods and/or models for the extraction of quantitative indices from images and signals represent an ever-evolving research area.

This Special Issue, therefore, will showcase original research and review articles on recent advances, technologies, and applications, as well as new challenges, in the field of biomedical images and signal processing.

Potential topics include, but are not limited to, the following:

  • Innovative detectors in biomedical image and signal acquisition;
  • New trends in biomedical imaging methodologies, such as magnetic resonance imaging, nuclear medicine imaging, computed tomography, and echography;
  • Hybrid biomedical imaging technologies;
  • Advances in one-dimensional and multi-dimensional signal processing techniques;
  • Machine learning and deep learning for biomedical image and signal processing;
  • Explainable artificial intelligence for biomedical image and signal processing.

Dr. Maria Filomena Santarelli
Dr. Vincenzo Positano
Dr. Nicola Vanello
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors 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 2600 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

  • biomedical imaging
  • biomedical signal processing
  • biomedical image processing

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Published Papers (1 paper)

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Research

17 pages, 2731 KB  
Article
MCM-UNet++: A Hybrid Soft Computing Framework for Multi-Scale Polyp Segmentation via Enhanced Global Context and Adaptive Feature Fusion
by Jinmei Li, Ming Zhao, Quan Du, Song Lu and Shenglung Peng
Sensors 2026, 26(11), 3380; https://doi.org/10.3390/s26113380 - 26 May 2026
Viewed by 391
Abstract
Colonoscopy polyp segmentation is important for colorectal cancer screening, yet it remains challenging because polyps exhibit large morphological variation, weak lesion–background contrast, blurred boundaries, and severe foreground–background imbalance. To address these issues, this paper presents MCM-UNet++, a hybrid U-Net++-based segmentation framework that combines [...] Read more.
Colonoscopy polyp segmentation is important for colorectal cancer screening, yet it remains challenging because polyps exhibit large morphological variation, weak lesion–background contrast, blurred boundaries, and severe foreground–background imbalance. To address these issues, this paper presents MCM-UNet++, a hybrid U-Net++-based segmentation framework that combines three targeted enhancements. First, a Multi-Axis Transformer Block (MATransformerBlock) is incorporated into convolutional feature blocks to model long-range horizontal and vertical dependencies with lower complexity than dense global self-attention. Second, a Cross-Channel Mixing (CCM) module is used in nested skip fusion paths to recalibrate the channel and spatial responses and reduce redundant feature transmissions. Third, a Multi-Objective Adaptive Loss (MOALoss) combines focal, Dice, and boundary-aware terms with learnable weights to improve supervision for small regions and ambiguous boundaries. Experiments on four public polyp segmentation datasets (Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, and ETIS-Larib) show competitive performance against the selected baseline methods, with Dice/IoU scores of 0.9563/0.9278 on Kvasir-SEG and 0.8593/0.7896 on CVC-ColonDB. These results indicate that the proposed components can improve benchmark-level polyp segmentation performance, while broader validation is still required before clinical deployment. Full article
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