改进的嵌套UNet架构，提升生物标志物分割的多尺度建模与边界精度。
HistoSeg++: Delving deeper with attention and multiscale feature fusion for biomarker segmentation
- 引入内外注意力与通道重校准，增强多尺度特征捕捉能力
- 在三个公开数据集上达到优于现有嵌套UNet方法的分割性能
- 适合需要高精度边界分割的病理图像分析任务
医学图像中生物标志物的分割是生物信息学和生物医学应用中图像分析的第一步。尽管已有进展，现有方法仍难以有效捕捉多尺度信息并跨数据集进行上采样，导致泛化能力不足。最近，嵌套UNet类架构在捕获多尺度上下文信息和有效上采样方面表现优异。本文提出一种新型嵌套UNet架构，通过内层与外层注意力单元强化上采样过程中的注意力聚焦，并结合挤压-激励模块实现通道级特征重校准，显著提升分割性能。此外，模型引入边缘感知损失，对边缘区域赋予更高权重以增强边界准确性。在三个公开基准数据集上进行了广泛测试，结果表明该方法在泛化性能上优于现有嵌套UNet方法。代码已开源：https://github.com/saadwazir/histosegplusplus。
原文摘要 · Abstract (English)
Segmentation of biomarkers in medical images is frequently viewed as a first step towards medical image analysis in any bioinformatics or biomedical application. Despite progress, existing methods still struggle to capture information at multiple scales and to perform upsampling effectively across different datasets. These shortcomings often result in suboptimal generalization capabilities. Recently, architectures belonging to the Nested-UNet family excel in capturing multiscale contextual information and upsample them effectively. In this work, We propose a novel Nested-UNet architecture that effectively captures multi-scale contextual information. It includes inner and outer attention units to enhance focus during upsampling, along with channel-wise feature recalibration using squeeze-and-excitation modules, leading to improved segmentation performance. Additionally, the architecture integrates an edge-aware loss to emphasize boundary accuracy by assigning greater importance to edge regions. Tested extensively on three publicly available benchmark datasets. Our method demonstrates a generalization performance superior to existing Nested-UNet methods. Code: https://github.com/saadwazir/histosegplusplus
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