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- W4387422476 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 ." @default.
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- W4387422476 date "2023-01-01" @default.
- W4387422476 modified "2023-10-08" @default.
- W4387422476 title "Decoupled Conditional Contrastive Learning with Variable Metadata for Prostate Lesion Detection" @default.
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- W4387422476 doi "https://doi.org/10.1007/978-3-031-44917-8_9" @default.
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