新解码器提升医学图像生物标志物分割精度
Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention
- 融合深度恢复与残差线性注意力，增强多尺度特征提取
- 在4个数据集上均超越现有最优方法，最高提升4.03%
- 适合需要高精度分割的医疗影像研究者使用
医学图像中生物标志物的分割对生物技术应用至关重要。尽管已有进展，基于Transformer和CNN的方法仍难以应对染色和形态变化，限制了特征提取。在样本量有限的医学图像分割任务中，近期最先进（SOTA）方法通过使用预训练编码器实现更高精度，而端到端方法表现较差。这源于编码器到解码器间丰富多尺度特征的有效传递困难以及解码器效率不足。为此，我们提出一种新架构，能捕捉多尺度局部与全局上下文信息，并设计了一种新型解码器，有效整合编码器特征，突出重要通道与区域，重建空间维度以提升分割精度。该方法兼容多种编码器，在四个数据集上的实验及消融研究中均优于现有SOTA方法，具体在MoNuSeg、DSB、电子显微镜和TNBC数据集上分别取得2.76%、3.12%、2.87%和4.03%的绝对性能提升。
原文摘要 · Abstract (English)
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
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