发表机构
School of Computer Science, University of Leeds（利兹大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对数字病理学中细胞分割与分类的冗余信息问题，提出SAF门控模块替换CellViT的传统跳跃连接，在PanNuke和MoNuSeg数据集上使mPQ达0.471，死亡类F1分数提升14.5个百分点。
AI 中文摘要
准确的细胞分割与分类是数字病理学的基础，可实现用于诊断和治疗规划的定量组织分析。通过跳跃连接融合多尺度特征的编解码器架构已成为该任务的主导范式，但标准直接跳跃连接会平等对待每个空间位置，导致解码器接收到冗余且可能冲突的信息。为解决此问题，人们引入了各种门控机制，但大多数仅作用于过滤编码器信息，忽略了来自解码器的全局上下文信息的益处。本研究提出将基于CellViT的模型中的传统跳跃连接替换为新型空间注意力融合（Spatial Attention Fusion, SAF）门控模块。每个SAF门控将编码器跳跃特征与上采样解码器特征拼接，通过两个带中间ReLU的逐点卷积压缩，再应用通道方向的softmax生成每个像素的“信任度热图”，该热图在每个空间位置总和为1，使网络能学习每个源最可信的位置。所得融合特征提升了模型检测少数类“死亡（Dead）”类的能力，进而提高了PanNuke数据集上的多类全景质量（mPQ）。在PanNuke和MoNuSeg数据集上，SAF门控与六种门控替代方案（包括无门控、注意力门控、挤压-激励、CBAM、交叉注意力和注意力特征融合）进行了比较，SAF门控取得了最高的mPQ（0.471），该提升主要源于与无门控的CellViT基线相比，死亡类F1分数提高了14.5个百分点。
英文摘要
Accurate cell segmentation and classification are foundational to digital pathology, enabling quantitative tissue analysis for diagnosis and treatment planning. Encoder-decoder architectures that fuse multi-scale features through skip connections have become the dominant paradigm for this task, yet standard direct skip connections treat every spatial location equally, which leads to redundant and potentially conflicting information reaching the decoder. To overcome this problem, various gating mechanisms have been introduced, but most of them operate solely on filtering encoder information, neglecting the benefit of global contextual information from the decoder. This study proposes replacing conventional skip connections in a CellViT-based model with a novel Spatial Attention Fusion (SAF) Gating module. Each SAF gate concatenates the encoder skip and upsampled decoder features, compresses them through two pointwise convolutions with an intermediate ReLU, and applies a channel-wise softmax to produce a per-pixel "heatmap of trust" that sums to unity at every spatial location, allowing the network to learn where each source is most trustworthy. The resulting fused features improve the model's ability to detect the minority "Dead" class, which in turn enhances the multi-class panoptic quality (mPQ) on the PanNuke dataset. SAF Gating is compared against six gating alternatives including no gating, attention gates, squeeze-and-excitation, CBAM, cross-attention, and attentional feature fusion on PanNuke and MoNuSeg datasets. SAF Gating achieves the highest mPQ (0.471), a gain driven primarily by a 14.5-point improvement in Dead-class F1 score compared to ungated CellViT baseline.
