HRMedSeg: Unlocking High-resolution Medical Image segmentation via Memory-efficient Attention Modeling (2504.06205v1)

Abstract: High-resolution segmentation is critical for precise disease diagnosis by extracting micro-imaging information from medical images. Existing transformer-based encoder-decoder frameworks have demonstrated remarkable versatility and zero-shot performance in medical segmentation. While beneficial, they usually require huge memory costs when handling large-size segmentation mask predictions, which are expensive to apply to real-world scenarios. To address this limitation, we propose a memory-efficient framework for high-resolution medical image segmentation, called HRMedSeg. Specifically, we first devise a lightweight gated vision transformer (LGViT) as our image encoder to model long-range dependencies with linear complexity. Then, we design an efficient cross-multiscale decoder (ECM-Decoder) to generate high-resolution segmentation masks. Moreover, we utilize feature distillation during pretraining to unleash the potential of our proposed model. Extensive experiments reveal that HRMedSeg outperforms state-of-the-arts in diverse high-resolution medical image segmentation tasks. In particular, HRMedSeg uses only 0.59GB GPU memory per batch during fine-tuning, demonstrating low training costs. Besides, when HRMedSeg meets the Segment Anything Model (SAM), our HRMedSegSAM takes 0.61% parameters of SAM-H. The code is available at https://github.com/xq141839/HRMedSeg.

Analysis of HRMedSeg: A Memory-Efficient Framework for High-Resolution Medical Image Segmentation

The paper presents HRMedSeg, a memory-efficient framework tailored for high-resolution medical image segmentation. This framework aims to address critical issues in the domain of medical imaging, particularly the significant memory consumption associated with existing transformer-based segmentation models.

Medical image segmentation is a pivotal component in diagnostic processes, offering the competence to differentiate between various tissues, organs, and potential pathologies within diverse high-resolution modalities such as dermoscopy, X-ray, and microscopy. Precision in segmentation facilitates microstructural analysis, crucial in advanced clinical diagnostics. Despite substantial progress in this area, traditional models often face challenges balancing computational efficiency and segmentation accuracy, especially under limited hardware resources.

HRMedSeg introduces a novel architecture incorporating a Lightweight Gated Vision Transformer (LGViT) and an Efficient Cross-Multiscale Decoder (ECM-Decoder). The LGViT is optimized to handle the hierarchical structure and relationships in medical images through linear attention mechanisms, distinctively employing dual-gated linear attention to ensure both efficiency and adequate expressive capacity. This mechanism effectively reduces the quadratic complexity typically seen in Vision Transformers, lowering the computational burden substantially while maintaining performance across various segmentation tasks.

In contrast to conventional models that demand high computational resources for multiscale feature extraction, HRMedSeg's ECM-Decoder leverages cross-multiscale strategies to refine low-resolution features, bypassing the need for resource-intensive pyramid decoding altogether. This design significantly alleviates memory consumption while preserving detail in high-resolution mask generation. The pretraining feature distillation process further enhances HGViT’s representation capability by distilling representations from large foundational models like SAM, ensuring robust performance in segmentation tasks.

Empirical results exhibit HRMedSeg's superiority over state-of-the-art methodologies, achieving higher accuracy with a fraction of the memory usage. Specifically, the framework demonstrates a 92.31% and 59.59% reduction in GPU memory requirements compared to baseline architectures like UNet and UNeXt, making it an attractive option for applications in teleport minimal computational environments.

From a theoretical standpoint, HRMedSeg's novel attention mechanism and decoding approach suggest new directions in transforming medical image segmentation architectures. Introducing lightweight, scalable models that retain high accuracy can disrupt current paradigms, facilitating broader application in real-world clinical settings where computational resources are constrained.

Future paths may investigate integrating such efficient architectures with emerging technologies like mobile healthcare diagnostics. There is also potential for further refinement of distillation techniques and adaptation strategies, aiming to generalize segmentation capabilities across unforeseen medical datasets, ultimately pushing towards universal segmentation models.

Overall, this work provides meaningful contributions through a thoughtful combination of theoretical innovations and practical applications, marking a step forward in the ongoing evolution of deep learning in medical image analysis.