ACC-UNet: A Completely Convolutional UNet model for the 2020s
Nabil Ibtehaz Daisuke Kihara
This decade is marked by the introduction of Vision Transformer, a radical
paradigm shift in broad computer vision. A similar trend is followed in medical
imaging, UNet, one of the most influential architectures, has been redesigned
with transformers. Recently, the efficacy of convolutional models in vision is
being reinvestigated by seminal works such as ConvNext, which elevates a ResNet
to Swin Transformer level. Deriving inspiration from this, we aim to improve a
purely convolutional UNet model so that it can be on par with the
transformer-based models, e.g, Swin-Unet or UCTransNet. We examined several
advantages of the transformer-based UNet models, primarily long-range
dependencies and cross-level skip connections. We attempted to emulate them
through convolution operations and thus propose, ACC-UNet, a completely
convolutional UNet model that brings the best of both worlds, the inherent
inductive biases of convnets with the design decisions of transformers.
ACC-UNet was evaluated on 5 different medical image segmentation benchmarks and
consistently outperformed convnets, transformers, and their hybrids. Notably,
ACC-UNet outperforms state-of-the-art models Swin-Unet and UCTransNet by $2.64
\pm 2.54\%$ and $0.45 \pm 1.61\%$ in terms of dice score, respectively, while
using a fraction of their parameters ($59.26\%$ and $24.24\%$). Our codes are
available at https://github.com/kiharalab/ACC-UNet.