The computer-aided disease diagnosis from radiomic data is important in many
medical applications. However, developing such a technique relies on annotating
radiological images, which is a time-consuming, labor-intensive, and expensive
process. In this work, we present the first novel collaborative self-supervised
learning method to solve the challenge of insufficient labeled radiomic data,
whose characteristics are different from text and image data. To achieve this,
we present two collaborative pretext tasks that explore the latent pathological
or biological relationships between regions of interest and the similarity and
dissimilarity information between subjects. Our method collaboratively learns
the robust latent feature representations from radiomic data in a
self-supervised manner to reduce human annotation efforts, which benefits the
disease diagnosis. We compared our proposed method with other state-of-the-art
self-supervised learning methods on a simulation study and two independent
datasets. Extensive experimental results demonstrated that our method
outperforms other self-supervised learning methods on both classification and
regression tasks. With further refinement, our method shows the potential
advantage in automatic disease diagnosis with large-scale unlabeled data
available.

本文提出了一种新型的协作自监督学习方法，探索兴趣区域之间的潜在病理或生物关系以及主体之间的相似性和差异性信息，无需大量的标记数据即可从辐射组学数据中学习准确的特征表示，进而提高计算机辅助疾病诊断效果。实验结果表明，该方法在分类和回归任务上优于现有的其他自监督学习方法，并具有大规模无标签数据进行自动疾病诊断的潜在优势。