Supervised contrastive loss helps uncover more robust features for photoacoustic prostate cancer identification
BackgroundPhotoacoustic spectral analysis has been demonstrated to be efficacious in the diagnosis of prostate cancer (PCa). With the incorporation of deep learning, its discrimination accuracy is progressively enhancing. Nevertheless, individual heterogeneity persists as a significant factor that i...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1592815/full |
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| author | Yingna Chen Yingna Chen Feifan Li Zhuoheng Dai Ying Liu Shengsong Huang Qian Cheng Qian Cheng |
| author_facet | Yingna Chen Yingna Chen Feifan Li Zhuoheng Dai Ying Liu Shengsong Huang Qian Cheng Qian Cheng |
| author_sort | Yingna Chen |
| collection | DOAJ |
| description | BackgroundPhotoacoustic spectral analysis has been demonstrated to be efficacious in the diagnosis of prostate cancer (PCa). With the incorporation of deep learning, its discrimination accuracy is progressively enhancing. Nevertheless, individual heterogeneity persists as a significant factor that impacts discrimination performance.ObjectiveExtracting more reliable features from intricate biological tissue and augmenting discrimination accuracy of the prostate cancer.MethodsSupervised contrastive learning is introduced to explore its performance in photoacoustic spectral feature extraction. Three distinct models, namely the CNN-based model, the supervised contrastive (SC) model, and the supervised contrastive loss adjust (SCL-adjust) model, have been compared, along with traditional feature extraction and machine learning-based methods.ResultsThe outcomes have indicated that the SCL-adjust model exhibits the optimal performance, its accuracy rate has increased by more than 10% compared with the traditional method. Besides, the features extracted from this model are more resilient, regardless of the presence of uniform or Gaussian noise and model transfer. Compared with CNN model, the transfer performance of the proposed model has improved by approximately 5%.ConclusionsSupervised contrast learning is integrated into photoacoustic spectrum analysis and its effectiveness is verified. A comprehensive analysis is conducted on the performance improvement of the proposed SCL-adjust model in photoacoustic prostate cancer diagnosis, its resistance to noise, and its adaptability to the data heterogeneity of different systems. |
| format | Article |
| id | doaj-art-8894c62d43a84b41b3f7cd2d24689f31 |
| institution | OA Journals |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-8894c62d43a84b41b3f7cd2d24689f312025-08-20T02:36:19ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-07-011510.3389/fonc.2025.15928151592815Supervised contrastive loss helps uncover more robust features for photoacoustic prostate cancer identificationYingna Chen0Yingna Chen1Feifan Li2Zhuoheng Dai3Ying Liu4Shengsong Huang5Qian Cheng6Qian Cheng7School of Information Engineering, College of Science & Technology Ningbo University, Ningbo, Zhejiang, ChinaSchool of Physics Science and Engineering, Tongji University, Shanghai, ChinaSchool of Information Engineering, College of Science & Technology Ningbo University, Ningbo, Zhejiang, ChinaSchool of Information Engineering, College of Science & Technology Ningbo University, Ningbo, Zhejiang, ChinaDepartment of Urology, Tongji Hospital, Tongji University School of Medicine, Shanghai, ChinaDepartment of Urology, Tongji Hospital, Tongji University School of Medicine, Shanghai, ChinaSchool of Physics Science and Engineering, Tongji University, Shanghai, ChinaThe National Key Laboratory of Autonomous Intelligent Unmanned Systems, China, The Frontiers Science Center for Intelligent Autonomous Systems, Ministry of Education, Shanghai, ChinaBackgroundPhotoacoustic spectral analysis has been demonstrated to be efficacious in the diagnosis of prostate cancer (PCa). With the incorporation of deep learning, its discrimination accuracy is progressively enhancing. Nevertheless, individual heterogeneity persists as a significant factor that impacts discrimination performance.ObjectiveExtracting more reliable features from intricate biological tissue and augmenting discrimination accuracy of the prostate cancer.MethodsSupervised contrastive learning is introduced to explore its performance in photoacoustic spectral feature extraction. Three distinct models, namely the CNN-based model, the supervised contrastive (SC) model, and the supervised contrastive loss adjust (SCL-adjust) model, have been compared, along with traditional feature extraction and machine learning-based methods.ResultsThe outcomes have indicated that the SCL-adjust model exhibits the optimal performance, its accuracy rate has increased by more than 10% compared with the traditional method. Besides, the features extracted from this model are more resilient, regardless of the presence of uniform or Gaussian noise and model transfer. Compared with CNN model, the transfer performance of the proposed model has improved by approximately 5%.ConclusionsSupervised contrast learning is integrated into photoacoustic spectrum analysis and its effectiveness is verified. A comprehensive analysis is conducted on the performance improvement of the proposed SCL-adjust model in photoacoustic prostate cancer diagnosis, its resistance to noise, and its adaptability to the data heterogeneity of different systems.https://www.frontiersin.org/articles/10.3389/fonc.2025.1592815/fullsupervised contrastive learningphotoacoustic spectral analysisprostate cancerrobust featureCNN |
| spellingShingle | Yingna Chen Yingna Chen Feifan Li Zhuoheng Dai Ying Liu Shengsong Huang Qian Cheng Qian Cheng Supervised contrastive loss helps uncover more robust features for photoacoustic prostate cancer identification Frontiers in Oncology supervised contrastive learning photoacoustic spectral analysis prostate cancer robust feature CNN |
| title | Supervised contrastive loss helps uncover more robust features for photoacoustic prostate cancer identification |
| title_full | Supervised contrastive loss helps uncover more robust features for photoacoustic prostate cancer identification |
| title_fullStr | Supervised contrastive loss helps uncover more robust features for photoacoustic prostate cancer identification |
| title_full_unstemmed | Supervised contrastive loss helps uncover more robust features for photoacoustic prostate cancer identification |
| title_short | Supervised contrastive loss helps uncover more robust features for photoacoustic prostate cancer identification |
| title_sort | supervised contrastive loss helps uncover more robust features for photoacoustic prostate cancer identification |
| topic | supervised contrastive learning photoacoustic spectral analysis prostate cancer robust feature CNN |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1592815/full |
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