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J. Imaging
Special Issues
Advances in Machine Learning for Biomedical Signal and Image Processing
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Advances in Machine Learning for Biomedical Signal and Image Processing

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 8428

Special Issue Editor

Prof. Dr. Lotfi Chaari

E-Mail Website
Guest Editor
Toulouse INP, University of Toulouse, Institut de Recherche en Informatique de Toulouse, 31400 Toulouse, France
Interests: machine learning; artificial intelligence; medical imaging; remote sensing; inverse problems

Special Issue Information

Dear Colleagues,

Machine learning (ML) is today being widely used as a solution to learn from data how to solve problems difficult to model in an analytical way. Due to the democratization of efficient and user-friendly computational facilities, many applications are taking advantage from ML tools. In this sense, the biomedical signal and image processing community is facing a great challenge and opportunity to integrate ML tools in applications such as diagnosis aid, reconstruction, restoration, and data analysis. From diagnosis to therapy, ML for biomedical signal and image processing is a hot research topic with high potential at both methodological and applicative levels.

The motivation behind this proposal lies in the emergence of the use of ML in the biomedical field. This Special Issue will contribute to outline recent advances in different applications handling biomedical signals and images with ML tools. The goal is to point out how ML and deep learning methods can solve various problems in biomedical signal and image processing such as segmentation, super-resolution, reconstruction, detection, etc.

The goal of this Special Issue is to bring together a number of recent methodological and applied advances in machine learning for biomedical data that are of particular interest to the signal and image processing community in view of their challenges and opportunities.

Indeed, the interest of both the machine learning and the medical imaging specialized research communities in the topic is clearly reflected by the organization of special sessions in conferences like ISBI 2020, WCCI 2020, BIOSTEC 2021, EUSIPCO 2021, and ICDHT 2021.

We request contributions presenting methodological and/or applied advances in any application handling biomedical signals or images using ML approaches. Scientifically-founded innovative and speculative research lines are welcome for proposal and evaluation.

Prof. Dr. Lotfi Chaari
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. Journal of Imaging 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

  • machine learning
  • deep learning
  • medical imaging
  • biomedical signal

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Published Papers (2 papers)

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Research

9 pages, 6384 KiB  
Article
Multi-Modality Microscopy Image Style Augmentation for Nuclei Segmentation
by Ye Liu, Sophia J. Wagner and Tingying Peng
J. Imaging 2022, 8(3), 71; https://doi.org/10.3390/jimaging8030071 - 11 Mar 2022
Cited by 5 | Viewed by 3088
Abstract
Annotating microscopy images for nuclei segmentation by medical experts is laborious and time-consuming. To leverage the few existing annotations, also across multiple modalities, we propose a novel microscopy-style augmentation technique based on a generative adversarial network (GAN). Unlike other style transfer methods, it [...] Read more.
Annotating microscopy images for nuclei segmentation by medical experts is laborious and time-consuming. To leverage the few existing annotations, also across multiple modalities, we propose a novel microscopy-style augmentation technique based on a generative adversarial network (GAN). Unlike other style transfer methods, it can not only deal with different cell assay types and lighting conditions, but also with different imaging modalities, such as bright-field and fluorescence microscopy. Using disentangled representations for content and style, we can preserve the structure of the original image while altering its style during augmentation. We evaluate our data augmentation on the 2018 Data Science Bowl dataset consisting of various cell assays, lighting conditions, and imaging modalities. With our style augmentation, the segmentation accuracy of the two top-ranked Mask R-CNN-based nuclei segmentation algorithms in the competition increases significantly. Thus, our augmentation technique renders the downstream task more robust to the test data heterogeneity and helps counteract class imbalance without resampling of minority classes. Full article
(This article belongs to the Special Issue Advances in Machine Learning for Biomedical Signal and Image Processing)
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14 pages, 4785 KiB  
Article
A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia
by Sonain Jamil and MuhibUr Rahman
J. Imaging 2022, 8(3), 70; https://doi.org/10.3390/jimaging8030070 - 10 Mar 2022
Cited by 17 | Viewed by 4271
Abstract
Cardiovascular diseases (CVDs) are the primary cause of death. Every year, many people die due to heart attacks. The electrocardiogram (ECG) signal plays a vital role in diagnosing CVDs. ECG signals provide us with information about the heartbeat. ECGs can detect cardiac arrhythmia. [...] Read more.
Cardiovascular diseases (CVDs) are the primary cause of death. Every year, many people die due to heart attacks. The electrocardiogram (ECG) signal plays a vital role in diagnosing CVDs. ECG signals provide us with information about the heartbeat. ECGs can detect cardiac arrhythmia. In this article, a novel deep-learning-based approach is proposed to classify ECG signals as normal and into sixteen arrhythmia classes. The ECG signal is preprocessed and converted into a 2D signal using continuous wavelet transform (CWT). The time–frequency domain representation of the CWT is given to the deep convolutional neural network (D-CNN) with an attention block to extract the spatial features vector (SFV). The attention block is proposed to capture global features. For dimensionality reduction in SFV, a novel clump of features (CoF) framework is proposed. The k-fold cross-validation is applied to obtain the reduced feature vector (RFV), and the RFV is given to the classifier to classify the arrhythmia class. The proposed framework achieves 99.84% accuracy with 100% sensitivity and 99.6% specificity. The proposed algorithm outperforms the state-of-the-art accuracy, F1-score, and sensitivity techniques. Full article
(This article belongs to the Special Issue Advances in Machine Learning for Biomedical Signal and Image Processing)
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