Improving Generalization of Medical Image Registration Foundation Model

Deformable registration is a fundamental task in medical image processing, aiming to achieve precise alignment by establishing nonlinear correspondences between images. Traditional methods offer good adaptability and interpretability but are limited by computational efficiency. Although deep learning approaches have significantly improved registration speed and accuracy, they often lack flexibility and generalizability across different datasets and tasks. In recent years, foundation models have emerged as a promising direction, leveraging large and diverse datasets to learn universal features and transformation patterns for image registration, thus demonstrating strong cross-task transferability. However, these models still face challenges in generalization and robustness when encountering novel anatomical structures, varying imaging conditions, or unseen modalities. To address these limitations, this paper incorporates Sharpness-Aware Minimization (SAM) into foundation models to enhance their generalization and robustness in medical image registration. By optimizing the flatness of the loss landscape, SAM improves model stability across diverse data distributions and strengthens its ability to handle complex clinical scenarios. Experimental results show that foundation models integrated with SAM achieve significant improvements in cross-dataset registration performance, offering new insights for the advancement of medical image registration technology. Our code is available at https://github.com/Promise13/fm_sam}{https://github.com/Promise13/fm\_sam.

DSCformer: A Dual-Branch Network Integrating Enhanced Dynamic Snake Convolution and SegFormer for Crack Segmentation

In construction quality monitoring, accurately detecting and segmenting cracks in concrete structures is paramount for safety and maintenance. Current convolutional neural networks (CNNs) have demonstrated strong performance in crack segmentation tasks, yet they often struggle with complex backgrounds and fail to capture fine-grained tubular structures fully. In contrast, Transformers excel at capturing global context but lack precision in detailed feature extraction. We introduce DSCformer, a novel hybrid model that integrates an enhanced Dynamic Snake Convolution (DSConv) with a Transformer architecture for crack segmentation to address these challenges. Our key contributions include the enhanced DSConv through a pyramid kernel for adaptive offset computation and a simultaneous bi-directional learnable offset iteration, significantly improving the model's performance to capture intricate crack patterns. Additionally, we propose a Weighted Convolutional Attention Module (WCAM), which refines channel attention, allowing for more precise and adaptive feature attention. We evaluate DSCformer on the Crack3238 and FIND datasets, achieving IoUs of 59.22\% and 87.24\%, respectively. The experimental results suggest that our DSCformer outperforms state-of-the-art methods across different datasets.