Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.7
2.3
Journals
Algorithms
Special Issues
Machine Learning Algorithms for Biomedical Image Analysis and Applications
share announcement

Machine Learning Algorithms for Biomedical Image Analysis and Applications

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 710

Special Issue Editors

Dr. Ines Prata Machado

E-Mail Website
Guest Editor
Department of Oncology, University of Cambridge, Cambridge CB2 1TN, UK
Interests: clinical data analytics; healthcare decision support systems
Dr. Francesco Prinzi

E-Mail Website
Guest Editor
1. Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127 Palermo, Italy
2. Department of Computer Science and Technology, University of Cambridge, Cambridge CB2 1TN, UK
Interests: medical image analysis; radiomics; machine learning; explainable AI; multimodal learning
Dr. Carmelo Militello

E-Mail Website
Guest Editor
Institute for High-Performance Computing and Networking (ICAR-CNR), National Research Council, 90146 Palermo, Italy
Interests: medical images analysis and quantification; radiomics; applied machine learning, explainable predictive models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the infusion of architectural and algorithmic innovations within the realm of machine learning has revolutionized medical image analysis. Despite these advancements, the assimilation of these models into clinical practice bears several challenges. The wide availability and heterogeneity of open data offer the opportunity to train increasingly ambitious models, but which harbor a myriad of pitfalls.

Such challenges include the need for multimodal training, data harmonization, training small dataset scenarios, etc. Additionally, stringent requirements for explainability and reliability imposed by regulatory agencies add further complexity to the integration of machine learning models in clinical settings. Addressing these challenges holds immense potential for transformative impacts on healthcare, particularly in advancing the concepts of precision and personalized medicine.

This Special Issue will provide a forum to publish original research papers covering state-of-the-art and novel algorithms, methodologies, and applications of computational methods for biomedical image analysis and quantification, as well as to implement predictive models for precision medicine and clinical decision support systems.

Topics of interest include, but are not limited to, the following:

  • Biomedical image analysis algorithms and applications;
  • Machine learning and deep learning methods for medical image analysis;
  • Multimodal learning;
  • Data integration;
  • Data harmonization;
  • Radiomics;
  • Explainable AI techniques for interpretable and transparent AI.

Dr. Ines Prata Machado
Dr. Francesco Prinzi
Dr. Carmelo Militello
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. 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 1600 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
  • precision medicine
  • health informatics
  • radiomics
  • machine-learning
  • deep-learning
  • clinical decision support systems
  • explainable artificial intelligence

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 7925 KiB  
Article
Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function
by Lei Zhang, Xiaoke Wang, Michael Rawson, Radu Balan, Edward H. Herskovits, Elias R. Melhem, Linda Chang, Ze Wang and Thomas Ernst
Algorithms 2024, 17(5), 215; https://doi.org/10.3390/a17050215 - 15 May 2024
Viewed by 491
Abstract
Purpose: Motion-induced magnetic resonance imaging (MRI) artifacts can deteriorate image quality and reduce diagnostic accuracy, but motion by human subjects is inevitable and can even be caused by involuntary physiological movements. Deep-learning-based motion correction methods might provide a solution. However, most studies have [...] Read more.
Purpose: Motion-induced magnetic resonance imaging (MRI) artifacts can deteriorate image quality and reduce diagnostic accuracy, but motion by human subjects is inevitable and can even be caused by involuntary physiological movements. Deep-learning-based motion correction methods might provide a solution. However, most studies have been based on directly applying existing models, and the trained models are rarely accessible. Therefore, we aim to develop and evaluate a deep-learning-based method (Motion Correction-Net, or MC-Net) for suppressing motion artifacts in brain MRI scans. Methods: A total of 57 subjects, providing 20,889 slices in four datasets, were used. Furthermore, 3T 3D sagittal magnetization-prepared rapid gradient-echo (MP-RAGE) and 2D axial fluid-attenuated inversion-recovery (FLAIR) sequences were acquired. The MC-Net was derived from a UNet combined with a two-stage multi-loss function. T1-weighted axial brain images contaminated with synthetic motions were used to train the network to remove motion artifacts. Evaluation used simulated T1- and T2-weighted axial, coronal, and sagittal images unseen during training, as well as T1-weighted images with motion artifacts from real scans. The performance indices included the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and visual reading scores from three blinded clinical readers. A one-sided Wilcoxon signed-rank test was used to compare reader scores, with p < 0.05 considered significant. Intraclass correlation coefficients (ICCs) were calculated for inter-rater evaluations. Results: The MC-Net outperformed other methods in terms of PSNR and SSIM for the T1 axial test set. The MC-Net significantly improved the quality of all T1-weighted images for all directions (i.e., the mean SSIM of axial, sagittal, and coronal slices improved from 0.77, 0.64, and 0.71 to 0.92, 0.75, and 0.84; the mean PSNR improved from 26.35, 24.03, and 24.55 to 29.72, 24.40, and 25.37, respectively) and for simulated as well as real motion artifacts, both using quantitative measures and visual scores. However, MC-Net performed poorly for images with untrained T2-weighted contrast because the T2 contrast was unseen during training and is different from T1 contrast. Conclusion: The proposed two-stage multi-loss MC-Net can effectively suppress motion artifacts in brain MRI without compromising image quality. Given the efficiency of MC-Net (with a single-image processing time of ~40 ms), it can potentially be used in clinical settings. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Biomedical Image Analysis and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop