重新思考解码器设计：利用深度到空间恢复和残差线性注意力改进生物标志物分割
Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention
AI总结：
提出一种结合多尺度上下文捕获与新型解码器（深度到空间恢复和残差线性注意力）的医学图像分割架构，在四个数据集上超越现有SOTA方法，绝对性能提升2.76%-4.03%。
摘要：
在医学图像中分割生物标志物对于各种生物技术应用至关重要。尽管取得了进展，基于Transformer和CNN的方法常常难以应对染色和形态的变化，限制了特征提取。在医学图像分割中，数据集通常样本量有限，最近的最先进（SOTA）方法通过利用预训练编码器实现更高精度，而端到端方法往往表现不佳。这是由于将丰富的多尺度特征从编码器有效转移到解码器存在挑战，以及解码器效率的局限性。为解决这些问题，我们提出了一种捕获多尺度局部和全局上下文信息的架构，以及一种新颖的解码器设计，该设计有效整合编码器特征，强调重要通道和区域，并重建空间维度以提高分割精度。我们的方法兼容各种编码器，在四个数据集上的实验和消融研究表明，其性能优于SOTA方法。具体而言，与现有SOTA方法相比，我们的方法在MoNuSeg上实现了2.76%的绝对性能提升，在DSB上提升3.12%，在电子显微镜数据集上提升2.87%，在TNBC数据集上提升4.03%。代码：https://github.com/saadwazir/MCADS-Decoder
英文摘要：
Segmenting biomarkers in medical images is crucial for various biotech applications. Despite advances, Transformer and CNN based methods often struggle with variations in staining and morphology, limiting feature extraction. In medical image segmentation, where datasets often have limited sample availability, recent state-of-the-art (SOTA) methods achieve higher accuracy by leveraging pre-trained encoders, whereas end-to-end methods tend to underperform. This is due to challenges in effectively transferring rich multiscale features from encoders to decoders, as well as limitations in decoder efficiency. To address these issues, we propose an architecture that captures multi-scale local and global contextual information and a novel decoder design, which effectively integrates features from the encoder, emphasizes important channels and regions, and reconstructs spatial dimensions to enhance segmentation accuracy. Our method, compatible with various encoders, outperforms SOTA methods, as demonstrated by experiments on four datasets and ablation studies. Specifically, our method achieves absolute performance gains of 2.76% on MoNuSeg, 3.12% on DSB, 2.87% on Electron Microscopy, and 4.03% on TNBC datasets compared to existing SOTA methods. Code: https://github.com/saadwazir/MCADS-Decoder
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
