Y-CA-Net: A Convolutional Attention Based Network for Volumetric Medical Image Segmentation

• URL: http://arxiv.org/abs/2410.01003v1
• Date: Tue, 1 Oct 2024 18:50:45 GMT
• Title: Y-CA-Net: A Convolutional Attention Based Network for Volumetric Medical Image Segmentation
• Authors: Muhammad Hamza Sharif, Muzammal Naseer, Mohammad Yaqub, Min Xu, Mohsen Guizani,
• Abstract summary: discriminative local features are key components for the performance of attention-based VS methods. We incorporate the convolutional encoder branch with transformer backbone to extract local and global features in a parallel manner. Y-CT-Net achieves competitive performance on multiple medical segmentation tasks.
• Score: 47.12719953712902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent attention-based volumetric segmentation (VS) methods have achieved remarkable performance in the medical domain which focuses on modeling long-range dependencies. However, for voxel-wise prediction tasks, discriminative local features are key components for the performance of the VS models which is missing in attention-based VS methods. Aiming at resolving this issue, we deliberately incorporate the convolutional encoder branch with transformer backbone to extract local and global features in a parallel manner and aggregate them in Cross Feature Mixer Module (CFMM) for better prediction of segmentation mask. Consequently, we observe that the derived model, Y-CT-Net, achieves competitive performance on multiple medical segmentation tasks. For example, on multi-organ segmentation, Y-CT-Net achieves an 82.4% dice score, surpassing well-tuned VS Transformer/CNN-like baselines UNETR/ResNet-3D by 2.9%/1.4%. With the success of Y-CT-Net, we extend this concept with hybrid attention models, that derived Y-CH-Net model, which brings a 3% improvement in terms of HD95 score for same segmentation task. The effectiveness of both models Y-CT-Net and Y-CH-Net verifies our hypothesis and motivates us to initiate the concept of Y-CA-Net, a versatile generic architecture based upon any two encoders and a decoder backbones, to fully exploit the complementary strengths of both convolution and attention mechanisms. Based on experimental results, we argue Y-CA-Net is a key player in achieving superior results for volumetric segmentation.

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