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

翻译：前列腺癌的早期诊断对有效治疗至关重要。多参数磁共振成像（mp-MRI）被广泛用于病灶检测。前列腺影像报告和数据系统（PI-RADS）通过定义病灶恶性程度的评分，规范了前列腺MRI的解读。PI-RADS数据可从放射学报告中直接获取，但存在报告间高度变异性的问题。我们提出一种新的对比损失函数，该函数利用每个样本包含多个标注者的弱元数据，并通过定义元数据置信度来利用报告间的变异性。通过将不同置信度的元数据与未标注数据结合到单一的条件对比损失函数中，我们在公开的PI-CAI挑战数据集上实现了病灶检测AUC提升3%。代码可在以下网址获取：https://github.com/camilleruppli/decoupled_ccl