Supervised contrastive loss helps uncover more robust features for photoacoustic prostate cancer identification

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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Bibliographic Details
Main Authors: Yingna Chen, Feifan Li, Zhuoheng Dai, Ying Liu, Shengsong Huang, Qian Cheng
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-07-01
Series:Frontiers in Oncology
Subjects:
supervised contrastive learning
photoacoustic spectral analysis
prostate cancer
robust feature
CNN
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2025.1592815/full
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