A Dual-Branch Fusion Network with Asymmetric Depthwise Convolutions for Whole-Body Tumor Segmentation

Wenze Fan

Keywords: Dual branch, Tumour segmentation, Asymmetric Depth- wise Convolutions

TL;DR: A dual-branch encoder has been developed to address the problem of whole-body tumor segmentation

Abstract:

Although deep learning models have made significant progress
in medical segmentation, they have encountered challenges in tumor
detection and segmentation tasks, particularly in complex whole-body
scanning scenarios.To address this issue, this paper proposes an improved
nn UNet model. Specifically, an efficient dual branch encoder has been
developed, with one branch using a special "Inception style depthwise
separable convolution" inspired by the Google Inception network, and the
other branch using ordinary residual connections. This model effectively
improves segmentation efficiency.Experiments on the MICCAI FLARE
2025 dataset demonstrated significant improvements in both segmen-
tation accuracy and efficiency. Our method achieved an average organ
Dice Similarity Coefficient (DSC) of 13.4% and a Normalized Surface
Dice (NSD) of 5.98% on the public validation set.The average inference
time is 11 seconds.

Submission Number: 11