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Algorithms
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Machine Learning in Medical Signal and Image Processing (4th Edition)
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Machine Learning in Medical Signal and Image Processing (4th Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1065

Special Issue Editor

Dr. Maryam Ravan

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, New York Institute of Technology (NYIT), NYC Campus, Room 810, 1855 Broadway, New York, NY 10023-7692, USA
Interests: signal processing; machine learning; biomedical engineering; microwave imaging; non-destructive testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research on the development and medical applications of machine learning algorithms to this Special Issue, “Machine Learning in Medical Signal and Image Processing (4th Edition)”.

We are looking for papers showcasing new, innovative machine learning approaches with medical applications, including, but not limited to, the following: biomedical signal and image processing; biosensors; bioinformatics and computational biology; neural, rehabilitation, cardiovascular, and clinical engineering; therapeutic and diagnostic systems; robotics; healthcare information systems and telemedicine; devices and technologies; and emerging topics in biomedical engineering.

Dr. Maryam Ravan
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. Algorithms is an international peer-reviewed open access monthly 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 1800 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
  • disease classification and prognosis prediction
  • deep learning (CNN, RNN, GAN, etc.) in brain–computer interfaces (BCIs) and medical images
  • radiological image processing (MRI, fMRI, CT scans, PET, ultrasound, X-ray, etc.)
  • clinical data processing (electrocardiography (ECG), electromyography (EMG), electroencephalography (EEG), etc.)
  • data fusion techniques
  • statistical pattern recognition
  • advanced artifact reduction
  • wearable sensors
  • virtual reality

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Related Special Issue

Published Papers (3 papers)

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Research

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23 pages, 1986 KB  
Article
GMHCA-MCBILSTM: A Gated Multi-Head Cross-Modal Attention-Based Network for Emotion Recognition Using Multi-Physiological Signals
by Xueping Li, Yanbo Li, Yuhang Li and Yuan Yang
Algorithms 2025, 18(10), 664; https://doi.org/10.3390/a18100664 (registering DOI) - 20 Oct 2025
Abstract
To address the limitations of the single-modal electroencephalogram (EEG), such as its single physiological dimension, weak anti-interference ability, and inability to fully reflect emotional states, this paper proposes a gated multi-head cross-attention module (GMHCA) for multimodal fusion of EEG, electrooculography (EOG),and electrodermal activity [...] Read more.
To address the limitations of the single-modal electroencephalogram (EEG), such as its single physiological dimension, weak anti-interference ability, and inability to fully reflect emotional states, this paper proposes a gated multi-head cross-attention module (GMHCA) for multimodal fusion of EEG, electrooculography (EOG),and electrodermal activity (EDA). This attention module employs three independent and parallel attention computation units to assign independent attention weights to different feature subsets across modalities. Combined with a modality complementarity metric, the gating mechanism suppresses redundant heads and enhances the information transmission of key heads. Through multi-head concatenation, cross-modal interaction results from different perspectives are fused. For the backbone network, a multi-scale convolution and bidirectional long short-term memory network (MC-BiLSTM) is designed for feature extraction, tailored to the characteristics of each modality. Experiments show that this method, which primarily fuses eight-channel EEG with peripheral physiological signals, achieves an emotion recognition accuracy of 89.45%, a 7.68% improvement over single-modal EEG. In addition, in cross-subject experiments conducted on the SEED-IV dataset, the EEG+EOG modality achieved a classification accuracy of 92.73%. All were significantly better than the baseline method. This fully demonstrates the effectiveness of the innovative GMHCA module architecture and MC-BiLSTM feature extraction network proposed in this paper for multimodal fusion methods. Through the novel attention gating mechanism, higher recognition accuracy is achieved while significantly reducing the number of EEG channels, providing new ideas and approaches based on attention mechanisms and gated fusion for multimodal emotion recognition in resource-constrained environments. Full article
(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (4th Edition))
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23 pages, 2817 KB  
Article
Characterizing and Optimizing Spatial Selectivity of Peripheral Nerve Stimulation Montages and Electrode Configurations In Silico
by Jonathan Brand, Ryan Kochis, Vasav Shah and Wentai Liu
Algorithms 2025, 18(10), 635; https://doi.org/10.3390/a18100635 - 9 Oct 2025
Viewed by 338
Abstract
Spatially selective nerve stimulation is an active area of research, with the capability to reduce side effects and increase the clinical efficacy of nerve stimulation technologies. Several research groups have demonstrated proof-of-concept devices capable of performing spatially selective stimulation with multi-contact cuff electrodes [...] Read more.
Spatially selective nerve stimulation is an active area of research, with the capability to reduce side effects and increase the clinical efficacy of nerve stimulation technologies. Several research groups have demonstrated proof-of-concept devices capable of performing spatially selective stimulation with multi-contact cuff electrodes in vivo; however, optimizing the technique is difficult due to the large possibility space granted by a multi-electrode cuff. Our work attempts to elucidate the most valuable stimulation montages (current ratios between stimulating electrodes) provided by a multi-contact cuff. We characterized the performance of five different montage types when stimulating fibers in different “electrode configurations”, with configurations including up to three rings of electrode contacts, 13 different counts of electrodes per ring, and five electrode arc lengths per electrode count (for 195 unique configurations). Selected montages included several methods from prior art, as well as our own. Among montage types, the most spatially selective stimulation was one we refer to as “X-Adjacent” stimulation, in which three adjacent electrodes are active per ring. Optimized X-adjacent montages achieved an average fiber specificity of 71.9% for single-ring electrode configurations when stimulating fibers located at a depth of two-thirds of the nerve radius, and an average fiber specificity of 77.2% for two-ring configurations. These values were the highest among montages tested, and in combination with our other metrics, led these montages to perform best in the majority of cost functions investigated. This success leads us to recommend X-Adjacent montages to researchers exploring spatially selective stimulation. Full article
(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (4th Edition))
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Review

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27 pages, 957 KB  
Review
Deep Learning for Brain MRI Tissue and Structure Segmentation: A Comprehensive Review
by Nedim Šišić and Peter Rogelj
Algorithms 2025, 18(10), 636; https://doi.org/10.3390/a18100636 - 9 Oct 2025
Viewed by 457
Abstract
Brain MRI segmentation plays a crucial role in neuroimaging studies and clinical trials by enabling the precise localization and quantification of brain tissues and structures. The advent of deep learning has transformed the field, offering accurate and fast tools for MRI segmentation. Nevertheless, [...] Read more.
Brain MRI segmentation plays a crucial role in neuroimaging studies and clinical trials by enabling the precise localization and quantification of brain tissues and structures. The advent of deep learning has transformed the field, offering accurate and fast tools for MRI segmentation. Nevertheless, several challenges limit the widespread applicability of these methods in practice. In this systematic review, we provide a comprehensive analysis of developments in deep learning-based segmentation of brain MRI in adults, segmenting the brain into tissues, structures, and regions of interest. We explore the key model factors influencing segmentation performance, including architectural design, choice of input size and model dimensionality, and generalization strategies. Furthermore, we address validation practices, which are particularly important given the scarcity of manual annotations, and identify the limitations of current methodologies. We present an extensive compilation of existing segmentation works and highlight the emerging trends and key results. Finally, we discuss the challenges and potential future directions in the field. Full article
(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (4th Edition))
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