ArXiv Preprint
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.
Zhiyuan Li, Hailong Li, Anca L. Ralescu, Jonathan R. Dillman, Nehal A. Parikh, Lili He
2023-02-20
