ASCON: Anatomy-aware Supervised Contrastive Learning Framework for
  Low-dose CT Denoising
Zhihao Chen Qi Gao Yi Zhang Hongming Shan
While various deep learning methods have been proposed for low-dose computed
tomography (CT) denoising, most of them leverage the normal-dose CT images as
the ground-truth to supervise the denoising process. These methods typically
ignore the inherent correlation within a single CT image, especially the
anatomical semantics of human tissues, and lack the interpretability on the
denoising process. In this paper, we propose a novel Anatomy-aware Supervised
CONtrastive learning framework, termed ASCON, which can explore the anatomical
semantics for low-dose CT denoising while providing anatomical
interpretability. The proposed ASCON consists of two novel designs: an
efficient self-attention-based U-Net (ESAU-Net) and a multi-scale anatomical
contrastive network (MAC-Net). First, to better capture global-local
interactions and adapt to the high-resolution input, an efficient ESAU-Net is
introduced by using a channel-wise self-attention mechanism. Second, MAC-Net
incorporates a patch-wise non-contrastive module to capture inherent anatomical
information and a pixel-wise contrastive module to maintain intrinsic
anatomical consistency. Extensive experimental results on two public low-dose
CT denoising datasets demonstrate superior performance of ASCON over
state-of-the-art models. Remarkably, our ASCON provides anatomical
interpretability for low-dose CT denoising for the first time. Source code is
available at https://github.com/hao1635/ASCON.