TY - GEN
T1 - Decoupled Conditional Contrastive Learning with Variable Metadata for Prostate Lesion Detection
AU - Ruppli, Camille
AU - Gori, Pietro
AU - Ardon, Roberto
AU - Bloch, Isabelle
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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.
AB - 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.
KW - Contrastive Learning
KW - Prostate cancer segmentation
KW - Semi-supervised Learning
U2 - 10.1007/978-3-031-44917-8_9
DO - 10.1007/978-3-031-44917-8_9
M3 - Conference contribution
AN - SCOPUS:85174724831
SN - 9783031471964
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 95
EP - 105
BT - Medical Image Learning with Limited and Noisy Data - 2nd International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Proceedings
A2 - Xue, Zhiyun
A2 - Antani, Sameer
A2 - Zamzmi, Ghada
A2 - Yang, Feng
A2 - Rajaraman, Sivaramakrishnan
A2 - Liang, Zhaohui
A2 - Huang, Sharon Xiaolei
A2 - Linguraru, Marius George
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd Workshop on Medical Image Learning with Noisy and Limited Data, MILLanD 2023
Y2 - 8 October 2023 through 8 October 2023
ER -
