Focal-UNet: UNet-like Focal Modulation for Medical Image Segmentation
This addresses segmentation accuracy issues for medical imaging applications, representing an incremental improvement over existing transformer-based methods.
The paper tackles the problem of blockiness and cropped edges in medical image segmentation by proposing Focal-UNet, a U-shaped architecture using focal modulation, which achieved a 1.68% higher DICE score and 0.89 better HD metric on the Synapse dataset and a 4.25% higher DICE score on the NeoPolyp dataset compared to Swin-UNet.
Recently, many attempts have been made to construct a transformer base U-shaped architecture, and new methods have been proposed that outperformed CNN-based rivals. However, serious problems such as blockiness and cropped edges in predicted masks remain because of transformers' patch partitioning operations. In this work, we propose a new U-shaped architecture for medical image segmentation with the help of the newly introduced focal modulation mechanism. The proposed architecture has asymmetric depths for the encoder and decoder. Due to the ability of the focal module to aggregate local and global features, our model could simultaneously benefit the wide receptive field of transformers and local viewing of CNNs. This helps the proposed method balance the local and global feature usage to outperform one of the most powerful transformer-based U-shaped models called Swin-UNet. We achieved a 1.68% higher DICE score and a 0.89 better HD metric on the Synapse dataset. Also, with extremely limited data, we had a 4.25% higher DICE score on the NeoPolyp dataset. Our implementations are available at: https://github.com/givkashi/Focal-UNet