Attention W-Net: Improved Skip Connections for better Representations

Segmentation of macro and microvascular structures in fundoscopic retinal images plays a crucial role in detection of multiple retinal and systemic diseases, yet it is a difficult problem to solve. Most deep learning approaches for this task involve an autoencoder based architecture, but they face several issues such as lack of enough parameters, overfitting when there are enough parameters and incompatibility between internal feature-spaces. Due to such issues, these techniques are hence not able to extract the best semantic information from the limited data present for such tasks. We propose Attention W-Net, a new U-Net based architecture for retinal vessel segmentation to address these problems. In this architecture with a LadderNet backbone, we have two main contributions: Attention Block and regularisation measures. Our Attention Block uses decoder features to attend over the encoder features from skip-connections during upsampling, resulting in higher compatibility when the encoder and decoder features are added. Our regularisation measures include image augmentation and modifications to the ResNet Block used, which prevent overfitting. With these additions, we observe an AUC and F1-Score of 0.8407 and 0.9833 — a sizeable improvement over its LadderNet backbone as well as competitive performance among the contemporary state-of-the-art methods.