Decoupled conditional contrastive learning with variable metadata for prostate lesion detection

2023/08/18 by Camille Ruppli, Pietro Gori, Ruppli, Camille +5
Biochemistry, Genetics and Molecular Biology · Medicine · #Cancer-related molecular mechanisms research #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Prostate Cancer Diagnosis and Treatment #Prostate Cancer Treatment and Research

paper · pdf · doi:10.48550/arxiv.2308.09542

openalex publication_date 2023/08/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Early diagnosis of prostate cancer is crucial for efficient treatment. Multi-parametric Magnetic Resonance Images (mp-MRI) are widely used for lesion detection. The Prostate Imaging Reporting and Data System (PI-RADS) has standardized interpretation of prostate MRI by defining a score for lesion malignancy. PI-RADS data is readily available from radiology reports but is subject to high inter-reports variability. We propose a new contrastive loss function that leverages weak metadata with multiple annotators per sample and takes advantage of inter-reports variability by defining metadata confidence. By combining metadata of varying confidence with unannotated data into a single conditional contrastive loss function, we report a 3% AUC increase on lesion detection on the public PI-CAI challenge dataset. Code is available at: https://github.com/camilleruppli/decoupled_ccl

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