[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118548-en":3,"doc-seo-118548-105":29,"detail-sidebar-cat-0-en-105":89},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":11},118548,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Machine Learning Algorithms for Biomedical Image Analysis and Their Applications - Special Issue Editorial","The editorial highlights how recent machine learning innovations have transformed medical image analysis while emphasizing barriers to clinical adoption. It focuses on opportunities created by abundant but heterogeneous data, alongside unmet needs such as multimodal training, data harmonization, and learning in small-data scenarios. It also addresses additional complexity from regulatory demands for explainability and reliability. The issue gathers studies across MRI, CT, ECG, PET-CT, and histopathology, covering tasks like motion correction, fracture detection, classification, radiomics, and predictive modeling to support precision medicine and personalized care.","algorithms   \nEditorial  \nMachine Learning Algorithms for Biomedical Image Analysis and Their Applications  \nFrancesco Prinzi 1, Ines Prata Machado 2 and Carmelo Militello 3, *  \nReceived: 2 April 2025  \nAccepted: 29 May 2025  \nPublished: 4 June 2025  \nCitation: Prinzi, F.; Machado, I.P.; Militello, C. Machine Learning Algorithms for Biomedical Image Analysis and Their Applications. Algorithms 2025, 18, 337. [https://](https://)[ ](https://)[doi.org/10.3390/a18060337](doi.org/10.3390/a18060337)  \n[Copyright:](Copyright:) © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127 Palermo, Italy  \n2 Department of Oncology, University of Cambridge, Cambridge CB2 1TN, UK  \n3 Institute for High-Performance Computing and Networking (ICAR-CNR), National Research Council, 90146 Palermo, Italy  \n* Correspondence: carmelo.militello@cnr.it  \nIn recent years, architectural and algorithmic innovations in machine learning have revolutionized the analysis of medical images. Despite these advances, integrating these models into clinical practice comes with several challenges. The wide availability of data and their heterogeneity offer opportunities for us to train increasingly ambitious models. However, several challenges still need to be addressed: the need for multimodal training, data harmonization, the training of small dataset scenarios, etc. [1] . In addition, explainability and reliability requirements imposed by regulatory agencies add further complexity to the integration of machine learning models into clinical settings. As a consequence, it is essential to address these challenges to advance precision and personalized medicine.  \nThis Special Issue contains published articles on significant advancements in the application of artificial intelligence (AI) and machine learning (ML). Several image modalities, including magnetic resonance imaging (MRI), computed tomography (CT), electrocardiography (ECG), positron emission tomography–computed tomography (PET-CT), and histopathology were included in these articles. Motion correction, fracture detection, disease classification, radiomics, and predictive modelling were the tasks investigated, emphasizing the potential for AI-driven solutions in medical imaging tasks.  \nRadiomics is an innovative framework for medical image analysis [2] . Thousands of features can be extracted of imaging patterns associated with clinical features or disease outcomes [3] . Dhesi et al. [4] developed and validated a machine learning model that uses radiomic features extracted from PET-CT scans to predict the future growth rate of abdominal aortic aneurysms. Their study tested several machine learning models using the radiomic features mentioned above, obtaining promising results.  \nHowever, the ability of deep architectures to extract high-and low-level features makes deep-feature models much more informative than radiomic ones. From this perspective, Pandey et al. [5] selected three different deep architectures, fine-tuned them, and used them as base models, combined with a Bayesian-based probabilistic ensemble learning method, for fracture detection in cervical spine CT images. The proposed method considers the prediction uncertainty of the base models and combines the predictions obtained from each of them to improve its overall performance significantly. Heart disease is the leading cause of death worldwide, making the early, accurate, and effective diagnosis of heart disease crucial to saving lives. However, making manual interpretations of ECG imaging—a primary non-invasive method used to identify cardiac abnormalities—for heart disease ","cbCaikam0ljeTtew","https://ap.wps.com/l/cbCaikam0ljeTtew","pdf",128593,1,3,"English","en",105,"# Editorial overview\n## Clinical integration challenges\n## Scope of included image modalities\n## Featured application themes","[{\"question\":\"What major challenges hinder deploying machine learning models in clinical practice?\",\"answer\":\"Key challenges include multimodal training requirements, data harmonization across heterogeneous sources, and performance under small-dataset conditions. Regulatory expectations for explainability and reliability further complicate integration.\"},{\"question\":\"Which medical imaging modalities are covered in this special issue?\",\"answer\":\"The issue includes MRI, CT, ECG, PET-CT, and histopathology. It discusses how AI/ML approaches address tasks across these modalities.\"},{\"question\":\"What types of tasks and applications are emphasized in the included studies?\",\"answer\":\"Reported tasks include motion correction, fracture detection, disease classification, radiomics, and predictive modeling. The focus is on enabling AI-driven solutions for medical imaging workflows.\"}]","Machine Learning Algorithms for Biomedical Image Analysis and Their Applications - Special Issue Editorial | PDF",1785684089,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":28},"machine-learning-algorithms-for-biomedical-image-analysis-and-their-applications-special-issue-editorial","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":21},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/machine-learning-algorithms-for-biomedical-image-analysis-and-their-applications-special-issue-editorial/118548/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What major challenges hinder deploying machine learning models in clinical practice?","Question",{"text":73,"@type":74},"Key challenges include multimodal training requirements, data harmonization across heterogeneous sources, and performance under small-dataset conditions. Regulatory expectations for explainability and reliability further complicate integration.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"Which medical imaging modalities are covered in this special issue?",{"text":78,"@type":74},"The issue includes MRI, CT, ECG, PET-CT, and histopathology. It discusses how AI/ML approaches address tasks across these modalities.",{"name":80,"@type":71,"acceptedAnswer":81},"What types of tasks and applications are emphasized in the included studies?",{"text":82,"@type":74},"Reported tasks include motion correction, fracture detection, disease classification, radiomics, and predictive modeling. 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