Automatic Classification of Focal Liver Lesions Based on Multi-Sequence MRI.
Accurate and automated diagnosis of focal liver lesions is critical for effective radiological practice and patient treatment planning. This study presents a deep learning model specifically developed for classifying focal liver lesions across eight different MRI sequences, categorizing them into se...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 1986 - 1999 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187278965&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278965 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278965 187278965 187278965 10.1007/s10278-024-01326-0 187278965 ppf: 1986 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Classification of Focal Liver Lesions Based on Multi-Sequence MRI. aug: au: Hu, Mingfang Wang, Shuxin Wu, Mingjie Zhuang, Ting Liu, Xiaoqing Zhang, Yuqin affil: https://ror.org/03et85d35 Health Science Center, Ningbo University, 315000, Ningbo, China sug: subj: Deep Learning Methods Prediction Models Liver Neoplasms Classification Magnetic Resonance Imaging Methods Automation Diagnosis, Computer Assisted Methods Human Funding Source Conceptual Framework Adenoma Classification Algorithms Cysts Physiopathology Hemangioma Physiopathology Liver Physiopathology Machine Learning Methods Information Science Sensitivity and Specificity Radiologists Psychosocial Factors Radiology Service Specialties, Medical Methods Workflow Tomography, X-Ray Computed Methods Descriptive Statistics Comparative Studies Confidence Intervals ROC Curve ab: Accurate and automated diagnosis of focal liver lesions is critical for effective radiological practice and patient treatment planning. This study presents a deep learning model specifically developed for classifying focal liver lesions across eight different MRI sequences, categorizing them into seven distinct classes. The model includes a feature extraction module that derives multi-level representations of the lesions, a feature fusion attention module to integrate contextual information from the various sequences, and an attention-guided data augmentation module to enrich the training dataset. The proposed model achieved a patient-wise classification accuracy of 0.9302 and a lesion-wise accuracy of 0.8592, along with an F1-score of 0.8395, a recall of 0.8296, and a precision of 0.8551. These findings demonstrate the effectiveness of combining multi-sequence MRI with advanced deep learning methodologies, providing a robust tool to support radiologists in accurately classifying liver lesions in clinical settings. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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