A novel collaborative self-supervised learning method for radiomic data.

ABSTRACT: The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on labeling 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 of information between subjects. Our method collaboratively learns the robust latent feature representations from radiomic data in a self-supervised manner

SUBMITTER: Li Z 

PROVIDER: S-EPMC10440826 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature