Decoupled conditional contrastive learning with variable metadata for
  prostate lesion detection
Camille Ruppli Pietro Gori Roberto Ardon Isabelle Bloch
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