DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets

by   Shujun Wang, et al.

Deep convolutional neural networks have significantly boosted the performance of fundus image segmentation when test datasets have the same distribution as the training datasets. However, in clinical practice, medical images often exhibit variations in appearance for various reasons, e.g., different scanner vendors and image quality. These distribution discrepancies could lead the deep networks to over-fit on the training datasets and lack generalization ability on the unseen test datasets. To alleviate this issue, we present a novel Domain-oriented Feature Embedding (DoFE) framework to improve the generalization ability of CNNs on unseen target domains by exploring the knowledge from multiple source domains. Our DoFE framework dynamically enriches the image features with additional domain prior knowledge learned from multi-source domains to make the semantic features more discriminative. Specifically, we introduce a Domain Knowledge Pool to learn and memorize the prior information extracted from multi-source domains. Then the original image features are augmented with domain-oriented aggregated features, which are induced from the knowledge pool based on the similarity between the input image and multi-source domain images. We further design a novel domain code prediction branch to infer this similarity and employ an attention-guided mechanism to dynamically combine the aggregated features with the semantic features. We comprehensively evaluate our DoFE framework on two fundus image segmentation tasks, including the optic cup and disc segmentation and vessel segmentation. Our DoFE framework generates satisfying segmentation results on unseen datasets and surpasses other domain generalization and network regularization methods.


page 1

page 4

page 8


HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation

Modern deep neural networks struggle to transfer knowledge and generaliz...

Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation

Although recent years have witnessed the great success of convolutional ...

Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary

Domain generalization typically requires data from multiple source domai...

Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration

For medical image analysis, segmentation models trained on one or severa...

Modality-Agnostic Debiasing for Single Domain Generalization

Deep neural networks (DNNs) usually fail to generalize well to outside o...

Domain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whitening

Contemporary methods have shown promising results on cardiac image segme...

Dynamically Decoding Source Domain Knowledge For Unseen Domain Generalization

Domain generalization is an important problem which has gain much attent...

Please sign up or login with your details

Forgot password? Click here to reset