GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images
This work addresses the need for interpretable and controllable feature representations in medical imaging, which can aid in downstream tasks and synthetic data generation, but it appears incremental as it builds on existing flow-based models.
The authors tackled the problem of learning disentangled feature representations in medical images by proposing GLOWin, a flow-based invertible generative framework, and demonstrated its effectiveness on a brain tumor MR dataset with quantitative and qualitative results.
Disentangled representations can be useful in many downstream tasks, help to make deep learning models more interpretable, and allow for control over features of synthetically generated images that can be useful in training other models that require a large number of labelled or unlabelled data. Recently, flow-based generative models have been proposed to generate realistic images by directly modeling the data distribution with invertible functions. In this work, we propose a new flow-based generative model framework, named GLOWin, that is end-to-end invertible and able to learn disentangled representations. Feature disentanglement is achieved by factorizing the latent space into components such that each component learns the representation for one generative factor. Comprehensive experiments have been conducted to evaluate the proposed method on a public brain tumor MR dataset. Quantitative and qualitative results suggest that the proposed method is effective in disentangling the features from complex medical images.