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Multimodal Information Processing for Biomedical Signal and Image Analysis

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 5075

Special Issue Editors

Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: biomedical engineering; signal processing; signal instrumentation; monitoring devices
Special Issues, Collections and Topics in MDPI journals
Instituto de Telecomunicações, Universidade do Porto, 162400 Porto, Portugal
Interests: biomedical signal processing; biomedical imaging; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Recent advances in signal processing and deep learning have shown game-changing potential in how data sensed from human bodies can be used to extract useful diagnostic information, for screening, long-term continuous monitoring, and clinical practice. 

Unlocking the full potential of information processing applications in biomedical signal and image analysis passes through the definition of novel approaches able to conveniently extract information from the joint observations of measurements coming from multiple modalities. Such methods are able to leverage the intrinsic correlation existing among different modalities, to help enhance the quality of such signals. Moreover, the joint analysis of measurements gathered from different modalities can provide a more comprehensive characterization of a specific organ, apparatus, or process. In general, multimodal data represents a valuable source of side information to optimally process information embedded in biomedical signals. 

This Special Issue focuses on some of the recent developments in signal processing and deep learning applied to the analysis of multimodal biomedical signal and imaging data, which can be applied at different stages of computer-aided diagnosis systems, from signal acquisition to classification and prediction. 

Potential topics of this Special Issue include but are not limited to:

  • Multimodal signal denoising;
  • Biomedical signal and image segmentation in the presence of side information;
  • Multimodal data fusion for classification and prediction;
  • Multimodal deep learning for inverse problems in biomedical imaging;
  • Explainable multimodal deep learning systems.

Dr. Javier Garcia-Casado
Dr. Francesco Renna
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. 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 signals
  • Medical imaging
  • Deep learning
  • Signal processing
  • Multimodal data
  • Sensor fusion
  • Side information
  • Image recovery
  • Signal acquisition
  • Segmentation
  • Classification

Published Papers (2 papers)

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Research

13 pages, 3305 KiB  
Article
Deep Learning with Multimodal Integration for Predicting Recurrence in Patients with Non-Small Cell Lung Cancer
by Gihyeon Kim, Sehwa Moon and Jang-Hwan Choi
Sensors 2022, 22(17), 6594; https://0-doi-org.brum.beds.ac.uk/10.3390/s22176594 - 31 Aug 2022
Cited by 5 | Viewed by 2254
Abstract
Due to high recurrence rates in patients with non-small cell lung cancer (NSCLC), medical professionals need extremely accurate diagnostic methods to prevent bleak prognoses. However, even the most commonly used diagnostic method, the TNM staging system, which describes the tumor-size, nodal-involvement, and presence [...] Read more.
Due to high recurrence rates in patients with non-small cell lung cancer (NSCLC), medical professionals need extremely accurate diagnostic methods to prevent bleak prognoses. However, even the most commonly used diagnostic method, the TNM staging system, which describes the tumor-size, nodal-involvement, and presence of metastasis, is often inaccurate in predicting NSCLC recurrence. These limitations make it difficult for clinicians to tailor treatments to individual patients. Here, we propose a novel approach, which applies deep learning to an ensemble-based method that exploits patient-derived, multi-modal data. This will aid clinicians in successfully identifying patients at high risk of recurrence and improve treatment planning. Full article
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19 pages, 922 KiB  
Article
Identification of Characteristic Points in Multivariate Physiological Signals by Sensor Fusion and Multi-Task Deep Networks
by Matteo Rossi, Giulia Alessandrelli, Andra Dombrovschi, Dario Bovio, Caterina Salito, Luca Mainardi and Pietro Cerveri
Sensors 2022, 22(7), 2684; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072684 - 31 Mar 2022
Cited by 6 | Viewed by 1854
Abstract
Identification of characteristic points in physiological signals, such as the peak of the R wave in the electrocardiogram and the peak of the systolic wave of the photopletismogram, is a fundamental step for the quantification of clinical parameters, such as the pulse transit [...] Read more.
Identification of characteristic points in physiological signals, such as the peak of the R wave in the electrocardiogram and the peak of the systolic wave of the photopletismogram, is a fundamental step for the quantification of clinical parameters, such as the pulse transit time. In this work, we presented a novel neural architecture, called eMTUnet, to automate point identification in multivariate signals acquired with a chest-worn device. The eMTUnet consists of a single deep network capable of performing three tasks simultaneously: (i) localization in time of characteristic points (labeling task), (ii) evaluation of the quality of signals (classification task); (iii) estimation of the reliability of classification (reliability task). Preliminary results in overnight monitoring showcased the ability to detect characteristic points in the four signals with a recall index of about 1.00, 0.90, 0.90, and 0.80, respectively. The accuracy of the signal quality classification was about 0.90, on average over four different classes. The average confidence of the correctly classified signals, against the misclassifications, was 0.93 vs. 0.52, proving the worthiness of the confidence index, which may better qualify the point identification. From the achieved outcomes, we point out that high-quality segmentation and classification are both ensured, which brings the use of a multi-modal framework, composed of wearable sensors and artificial intelligence, incrementally closer to clinical translation. Full article
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