Incorporating External Information in Tissue Subtyping: A Topic Modeling Approach.

ABSTRACT: Probabilistic topic models, have been widely deployed for various applications such as learning disease or tissue subtypes. Yet, learning the parameters of such models is usually an ill-posed problem and may result in losing valuable information about disease severity. A common approach is to add a discriminative loss term to the generative model's loss in order to learn a representation that is also predictive of disease severity. However, finding a balance between these two losses is not straightforward. We propose an alternative way in this paper. We develop a framework which allows for incorporating external covariates into the generative model's approximate posterior. These covariates can have more discriminative power for disease severity compared to the representation that we extrac

SUBMITTER: Saeedi A 

PROVIDER: S-EPMC8797254 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature