MAP: Domain Generalization via Meta-Learning on Anatomy-Consistent Pseudo-Modalities
This addresses domain generalization for retinal vessel segmentation, which is crucial for clinical applicability, but it is incremental as it builds on existing meta-learning and pseudo-modality techniques.
The paper tackled the problem of limited generalization of deep models to unseen domains in retinal vessel segmentation by proposing MAP, a meta-learning method that uses anatomy-consistent pseudo-modalities, resulting in substantially better generalizability across seven public datasets.
Deep models suffer from limited generalization capability to unseen domains, which has severely hindered their clinical applicability. Specifically for the retinal vessel segmentation task, although the model is supposed to learn the anatomy of the target, it can be distracted by confounding factors like intensity and contrast. We propose Meta learning on Anatomy-consistent Pseudo-modalities (MAP), a method that improves model generalizability by learning structural features. We first leverage a feature extraction network to generate three distinct pseudo-modalities that share the vessel structure of the original image. Next, we use the episodic learning paradigm by selecting one of the pseudo-modalities as the meta-train dataset, and perform meta-testing on a continuous augmented image space generated through Dirichlet mixup of the remaining pseudo-modalities. Further, we introduce two loss functions that facilitate the model's focus on shape information by clustering the latent vectors obtained from images featuring identical vasculature. We evaluate our model on seven public datasets of various retinal imaging modalities and we conclude that MAP has substantially better generalizability. Our code is publically available at https://github.com/DeweiHu/MAP.