  We propose a novel transformer-style architecture called Global-Local Filter
Network (GLFNet) for medical image segmentation and demonstrate its
state-of-the-art performance. We replace the self-attention mechanism with a
combination of global-local filter blocks to optimize model efficiency. The
global filters extract features from the whole feature map whereas the local
filters are being adaptively created as 4x4 patches of the same feature map and
add restricted scale information. In particular, the feature extraction takes
place in the frequency domain rather than the commonly used spatial (image)
domain to facilitate faster computations. The fusion of information from both
spatial and frequency spaces creates an efficient model with regards to
complexity, required data and performance. We test GLFNet on three benchmark
datasets achieving state-of-the-art performance on all of them while being
almost twice as efficient in terms of GFLOP operations.
