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Mathematics
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New Trends in Machine Learning and Medical Imaging and Applications
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New Trends in Machine Learning and Medical Imaging and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 8628

Special Issue Editors

Dr. Hua Wang

E-Mail Website
Guest Editor
Department of Computer Science, Colorado School of Mines, Golden, CO, USA
Interests: machine learning; computer vision; bioinformatics; neuroinformatics; chemical informatics
Dr. Hongchang Gao

E-Mail Website
Guest Editor
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
Interests: machine learning; deep learning; optimization; data mining; biomedical data science

Special Issue Information

Dear Colleagues,

Medical imaging is an emerging research field that arises with recent advances in acquiring high-throughput multimodal imaging data. Its major task is to perform integrative analysis of structural, functional, and molecular imaging data. Bridging imaging factors and exploring their connections have the potential to provide important new insights into the phenotypic characteristics and/or disordered biological structures and functions, which in turn will impact the development of new diagnostic, therapeutic, and preventive approaches. However, the unprecedented scale and complexity of these imaging data sets have presented critical computational bottlenecks, requiring new concepts and enabling tools.

The main scope of this Special Issue is to help to advance scientific research within the broad field of machine learning in medical imaging, aiming to identify new cutting-edge techniques and their use in medical imaging. Topics of interests include but are not limited to:

  • Methods for genetic, epistatic, genomic, or multi-omic analysis of imaging phenotypes;
  • Methods for quantifying and exploring multidimensional individual genetic and phenotypic vulnerability, as well as electronic health records;
  • Novel methods to handle incomplete data, perform data harmonization across cohorts and modalities, and integrate multi-cohort data;
  • Distributed machine learning methods for integrating private multi-site medical imaging data;
  • Recent advancements of machine learning and/or data mining methods to facilitate medical imaging analytics;
  • Future of AI-assisted biomedical imaging devices in healthcare sectors;
  • Applications of AI techniques in diagnostic medical imaging systems.

Dr. Hua Wang
Dr. Hongchang Gao
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. Mathematics 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

  • machine learning
  • deep learning
  • data mining
  • federated learning
  • biomedical imaging
  • diagnostic imaging
  • radiology

Published Papers (4 papers)

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Research

14 pages, 1996 KiB  
Article
Ex-Vivo Hippocampus Segmentation Using Diffusion-Weighted MRI
by Haoteng Tang , Siyuan Dai, Eric M. Zou, Guodong Liu, Ryan Ahearn, Ryan Krafty, Michel Modo and Liang Zhan
Mathematics 2024, 12(7), 940; https://doi.org/10.3390/math12070940 - 22 Mar 2024
Viewed by 509
Abstract
The hippocampus is a crucial brain structure involved in memory formation, spatial navigation, emotional regulation, and learning. An accurate MRI image segmentation of the human hippocampus plays an important role in multiple neuro-imaging research and clinical practice, such as diagnosing neurological diseases and [...] Read more.
The hippocampus is a crucial brain structure involved in memory formation, spatial navigation, emotional regulation, and learning. An accurate MRI image segmentation of the human hippocampus plays an important role in multiple neuro-imaging research and clinical practice, such as diagnosing neurological diseases and guiding surgical interventions. While most hippocampus segmentation studies focus on using T1-weighted or T2-weighted MRI scans, we explore the use of diffusion-weighted MRI (dMRI), which offers unique insights into the microstructural properties of the hippocampus. Particularly, we utilize various anisotropy measures derived from diffusion MRI (dMRI), including fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, for a multi-contrast deep learning approach to hippocampus segmentation. To exploit the unique benefits offered by various contrasts in dMRI images for accurate hippocampus segmentation, we introduce an innovative multimodal deep learning architecture integrating cross-attention mechanisms. Our proposed framework comprises a multi-head encoder designed to transform each contrast of dMRI images into distinct latent spaces, generating separate image feature maps. Subsequently, we employ a gated cross-attention unit following the encoder, which facilitates the creation of attention maps between every pair of image contrasts. These attention maps serve to enrich the feature maps, thereby enhancing their effectiveness for the segmentation task. In the final stage, a decoder is employed to produce segmentation predictions utilizing the attention-enhanced feature maps. The experimental outcomes demonstrate the efficacy of our framework in hippocampus segmentation and highlight the benefits of using multi-contrast images over single-contrast images in diffusion MRI image segmentation. Full article
(This article belongs to the Special Issue New Trends in Machine Learning and Medical Imaging and Applications)
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27 pages, 4038 KiB  
Article
Enhancing Brain Tumor Segmentation Accuracy through Scalable Federated Learning with Advanced Data Privacy and Security Measures
by Faizan Ullah, Muhammad Nadeem, Mohammad Abrar, Farhan Amin, Abdu Salam and Salabat Khan
Mathematics 2023, 11(19), 4189; https://doi.org/10.3390/math11194189 - 07 Oct 2023
Cited by 4 | Viewed by 1387
Abstract
Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment while preserving patient data privacy and security. Traditional centralized approaches often encounter obstacles in data sharing due to privacy regulations and security concerns, hindering the development of advanced AI-based [...] Read more.
Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment while preserving patient data privacy and security. Traditional centralized approaches often encounter obstacles in data sharing due to privacy regulations and security concerns, hindering the development of advanced AI-based medical imaging applications. To overcome these challenges, this study proposes the utilization of federated learning. The proposed framework enables collaborative learning by training the segmentation model on distributed data from multiple medical institutions without sharing raw data. Leveraging the U-Net-based model architecture, renowned for its exceptional performance in semantic segmentation tasks, this study emphasizes the scalability of the proposed approach for large-scale deployment in medical imaging applications. The experimental results showcase the remarkable effectiveness of federated learning, significantly improving specificity to 0.96 and the dice coefficient to 0.89 with the increase in clients from 50 to 100. Furthermore, the proposed approach outperforms existing convolutional neural network (CNN)- and recurrent neural network (RNN)-based methods, achieving higher accuracy, enhanced performance, and increased efficiency. The findings of this research contribute to advancing the field of medical image segmentation while upholding data privacy and security. Full article
(This article belongs to the Special Issue New Trends in Machine Learning and Medical Imaging and Applications)
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22 pages, 3481 KiB  
Article
FedISM: Enhancing Data Imbalance via Shared Model in Federated Learning
by Wu-Chun Chung, Yan-Hui Lin and Sih-Han Fang
Mathematics 2023, 11(10), 2385; https://doi.org/10.3390/math11102385 - 20 May 2023
Viewed by 1537
Abstract
Considering the sensitivity of data in medical scenarios, federated learning (FL) is suitable for applications that require data privacy. Medical personnel can use the FL framework for machine learning to assist in analyzing large-scale data that are protected within the institution. However, not [...] Read more.
Considering the sensitivity of data in medical scenarios, federated learning (FL) is suitable for applications that require data privacy. Medical personnel can use the FL framework for machine learning to assist in analyzing large-scale data that are protected within the institution. However, not all clients have the same distribution of datasets, so data imbalance problems occur among clients. The main challenge is to overcome the performance degradation caused by low accuracy and the inability to converge the model. This paper proposes a FedISM method to enhance performance in the case of Non-Independent Identically Distribution (Non-IID). FedISM exploits a shared model trained on a candidate dataset before performing FL among clients. The Candidate Selection Mechanism (CSM) was proposed to effectively select the most suitable candidate among clients for training the shared model. Based on the proposed approaches, FedISM not only trains the shared model without sharing any raw data, but it also provides an optimal solution through the selection of the best shared model. To evaluate performance, the proposed FedISM was applied to classify coronavirus disease (COVID), pneumonia, normal, and viral pneumonia in the experiments. The Dirichlet process was also used to simulate a variety of imbalanced data distributions. Experimental results show that FedISM improves accuracy by up to 25% when privacy concerns regarding patient data are rising among medical institutions. Full article
(This article belongs to the Special Issue New Trends in Machine Learning and Medical Imaging and Applications)
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47 pages, 2701 KiB  
Article
Federated Learning for the Internet-of-Medical-Things: A Survey
by Vivek Kumar Prasad, Pronaya Bhattacharya, Darshil Maru, Sudeep Tanwar, Ashwin Verma, Arunendra Singh, Amod Kumar Tiwari, Ravi Sharma, Ahmed Alkhayyat, Florin-Emilian Țurcanu and Maria Simona Raboaca
Mathematics 2023, 11(1), 151; https://doi.org/10.3390/math11010151 - 28 Dec 2022
Cited by 11 | Viewed by 4361
Abstract
Recently, in healthcare organizations, real-time data have been collected from connected or implantable sensors, layered protocol stacks, lightweight communication frameworks, and end devices, named the Internet-of-Medical-Things (IoMT) ecosystems. IoMT is vital in driving healthcare analytics (HA) toward extracting meaningful data-driven insights. Recently, concerns [...] Read more.
Recently, in healthcare organizations, real-time data have been collected from connected or implantable sensors, layered protocol stacks, lightweight communication frameworks, and end devices, named the Internet-of-Medical-Things (IoMT) ecosystems. IoMT is vital in driving healthcare analytics (HA) toward extracting meaningful data-driven insights. Recently, concerns have been raised over data sharing over IoMT, and stored electronic health records (EHRs) forms due to privacy regulations. Thus, with less data, the analytics model is deemed inaccurate. Thus, a transformative shift has started in HA from centralized learning paradigms towards distributed or edge-learning paradigms. In distributed learning, federated learning (FL) allows for training on local data without explicit data-sharing requirements. However, FL suffers from a high degree of statistical heterogeneity of learning models, level of data partitions, and fragmentation, which jeopardizes its accuracy during the learning and updating process. Recent surveys of FL in healthcare have yet to discuss the challenges of massive distributed datasets, sparsification, and scalability concerns. Because of this gap, the survey highlights the potential integration of FL in IoMT, the FL aggregation policies, reference architecture, and the use of distributed learning models to support FL in IoMT ecosystems. A case study of a trusted cross-cluster-based FL, named Cross-FL, is presented, highlighting the gradient aggregation policy over remotely connected and networked hospitals. Performance analysis is conducted regarding system latency, model accuracy, and the trust of consensus mechanism. The distributed FL outperforms the centralized FL approaches by a potential margin, which makes it viable for real-IoMT prototypes. As potential outcomes, the proposed survey addresses key solutions and the potential of FL in IoMT to support distributed networked healthcare organizations. Full article
(This article belongs to the Special Issue New Trends in Machine Learning and Medical Imaging and Applications)
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