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.

翻译：从放射组学数据进行计算机辅助疾病诊断在许多医学应用中具有重要意义。然而，开发此类技术依赖于放射图像的标注，这是一个耗时、费力且成本高昂的过程。本文首次提出了一种新颖的协同自监督学习方法，以解决标注放射组学数据不足的挑战——这类数据的特性与文本及图像数据存在差异。为实现这一目标，我们设计了两个协同预训练任务，旨在探索感兴趣区域间的潜在病理或生物学关系，以及受试者之间的相似性与差异性信息。该方法以自监督方式从放射组学数据中协同学习鲁棒的潜在特征表示，从而减少人工标注工作量，进而促进疾病诊断。我们将所提方法与其他先进的自我监督学习方法在一个模拟研究和两个独立数据集上进行了比较。大量实验结果表明，该方法在分类和回归任务上均优于其他自监督学习方法。通过进一步优化，该方法在利用大规模未标注数据实现自动疾病诊断方面展现出潜在优势。