ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1592815
This article is part of the Research TopicAI-Empowered BiophotonicsView all articles
Supervised Contrastive Loss Helps Uncover More Robust Features for Photoacoustic Prostate Cancer Identification
Provisionally accepted
- 1College of Science and Technology, Ningbo University, Ningbo, China
- 2Tongji University, Shanghai, Shanghai Municipality, China
- 3Tongji Hospital Affiliated to Tongji University, Shanghai, Shanghai, China
- 4he National Key Laboratory of Autonomous Intelligent Unmanned Systems, P. R. China, Shanghai, China
- 5The Frontiers Science Center for Intelligent Autonomous Systems, Ministry of Education, Shanghai, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background: Photoacoustic 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. Objective: Extracting more reliable features from intricate biological tissue and augmenting discrimination accuracy of the prostate cancer. Methods: Supervised 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.Results: The 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%. Conclusions: Supervised 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.
Keywords: Supervised Contrastive Learning, Photoacoustic spectral analysis, prostate cancer, robust feature, CNN
Received: 25 Mar 2025; Accepted: 14 Jun 2025.
Copyright: © 2025 CHEN, Li, Dai, Liu, Huang and Cheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yingna CHEN, College of Science and Technology, Ningbo University, Ningbo, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.