Multiple perception contrastive learning for automated ovarian tumor classification in CT images.
Ovarian cancer is among the most common malignant tumours in women worldwide, and early identification is essential for enhancing patient survival chances. The development of automated and trustworthy diagnostic techniques is necessary because traditional CT picture processing mostly depends on the...
| Publicado en: | Abdominal Radiology Vol. 50; no. 9; pp. 4342 - 4359 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2025
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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=187189769&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187189769 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Sep2025 vid: 50 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187189769 183621631 10.1007/s00261-025-04879-y 187189769 ppf: 4342 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multiple perception contrastive learning for automated ovarian tumor classification in CT images. aug: au: Li, Lingwei Liu, Tongtong Wang, Peng Su, Lianzheng Wang, Lei Wang, Xinmiao Chen, Chidao affil: School of Medical Technology and Engineering, Henan School of Science and Technology, 471032, Luoyang, China sug: ab: Ovarian cancer is among the most common malignant tumours in women worldwide, and early identification is essential for enhancing patient survival chances. The development of automated and trustworthy diagnostic techniques is necessary because traditional CT picture processing mostly depends on the subjective assessment of radiologists, which can result in variability. Deep learning approaches in medical image analysis have advanced significantly, particularly showing considerable promise in the automatic categorisation of ovarian tumours. This research presents an automated diagnostic approach for ovarian tumour CT images utilising supervised contrastive learning and a Multiple Perception Encoder (MP Encoder). The approach incorporates T-Pro technology to augment data diversity and simulates semantic perturbations to increase the model's generalisation capability. The incorporation of Multi-Scale Perception Module (MSP Module) and Multi-Attention Module (MA Module) enhances the model's sensitivity to the intricate morphology and subtle characteristics of ovarian tumours, resulting in improved classification accuracy and robustness, ultimately achieving an average classification accuracy of 98.43%. Experimental results indicate the method's exceptional efficacy in ovarian tumour classification, particularly in cases involving tumours with intricate morphology or worse picture quality, thereby markedly enhancing classification accuracy. This advanced deep learning framework proficiently tackles the complexities of ovarian tumour CT image interpretation, offering clinicians enhanced diagnostic support and aiding in the optimisation of early detection and treatment strategies for ovarian cancer. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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