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: this https URL

通过结合多参数磁共振成像、前列腺成像报告与数据系统以及对比损失函数，提高了公开 PI-CAI 挑战数据集上的病变检测的 AUC 值。