Uncertainty-Gated Deformable Network for Breast Tumor Segmentation in MR Images
This work addresses breast cancer diagnosis by improving tumor segmentation in MRI, though it appears incremental as it builds on existing CNN and Transformer methods.
The paper tackled the problem of accurately segmenting irregular breast tumor shapes in MRI by proposing an uncertainty-gated deformable network that integrates CNN and Transformer features, achieving superior segmentation performance on two clinical datasets.
Accurate segmentation of breast tumors in magnetic resonance images (MRI) is essential for breast cancer diagnosis, yet existing methods face challenges in capturing irregular tumor shapes and effectively integrating local and global features. To address these limitations, we propose an uncertainty-gated deformable network to leverage the complementary information from CNN and Transformers. Specifically, we incorporates deformable feature modeling into both convolution and attention modules, enabling adaptive receptive fields for irregular tumor contours. We also design an Uncertainty-Gated Enhancing Module (U-GEM) to selectively exchange complementary features between CNN and Transformer based on pixel-wise uncertainty, enhancing both local and global representations. Additionally, a Boundary-sensitive Deep Supervision Loss is introduced to further improve tumor boundary delineation. Comprehensive experiments on two clinical breast MRI datasets demonstrate that our method achieves superior segmentation performance compared with state-of-the-art methods, highlighting its clinical potential for accurate breast tumor delineation.