Multimodal Contrastive Supervised Learning to Classify Clinical Significance MRI Regions on Prostate Cancer

人工智能 计算机科学 前列腺癌 元组 深度学习 模式识别(心理学) 学习迁移 机器学习 监督学习 参数统计 特征学习 医学 癌症 人工神经网络 数学 统计 离散数学 内科学
作者
Yesid Gutiérrez,John Arévalo,Fabio Martínez
标识
DOI:10.1109/embc48229.2022.9871243
摘要

Clinically significant regions (CSR), captured over multi-parametric MRI (mp-MRI) images, have emerged as a potential screening test for early prostate cancer detection and characterization. These sequences are able to quantify morphology, micro-circulation, and cellular density patterns that might be related to cancer disease. Nonetheless, this evaluation is mainly carried out by expert radiologists, introducing inter-reader variability in the diagnosis. Therefore, different deep learning models were proposed to support the diagnosis, but a proper representation of prostate lesions remains limited due to the non-alignment among sequences and the dependency of considerable amounts of labeled data for learning. The main limitation of such representation lies in the cross-entropy minimization that only exploits inter-class variation, being insufficient data augmentation and transfer learning strategies. This work introduces a Supervised Contrastive Learning (SCL) strategy that fully exploits the inter and intra-class variability of prostate lesions to robustly represent MRI regions. This strategy extracts lesion sample tuples, with positive and negative labels, regarding a query lesion. Such tuples are involved into an easy-positive, and semi-hard negative mining to project samples that better update the deep representation. The proposed learning strategy achieved an average ROC-AVC of 0.82, to characterize prostate cancer in MRI, using only the 60% of the available annotated data. Clinical relevance - A robust learning scheme that properly finds representations in limited data scenarios to classify clinically significant MRI regions on prostate cancer.
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