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\title{Difference-Deformable Convolution with Pseudo Scale Instance Map for
Cell Localization}



\author[1,1]{Chengyang Zhang }%
\author[2]{Jie Chen }%
\author[2]{Bo Li }%
\author[2]{Min Feng }%
\author[2]{Yongquan Yang }%
\author[2]{Qikui Zhu }%
\author[2]{Hong Bu }%
\affil[1]{the Department of Pathology and Institute of Clinical Pathology}%
\affil[2]{Affiliation not available}%


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  \date{May 07, 2026}


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\begin{abstract}
Difference-Deformable Convolution With Pseudo Scale Instance Map for
Cell Localization

Cell localization still faces two unresolved challenges: 1) the dramatic
variations in cell morphology, coupled with the heterogeneous intensity
distribution of lightly stained cells; 2) existing cell location maps
lack scale information, resulting in insufficient supervision for point
maps and inaccurate supervision for density maps. 1) To address the
first challenges, we introduce a novel gradient-aware and shape-adaptive
Difference-Deformable Convolution (DDConv), which enhances the model's
ro- bustness to color by leveraging gradient information while
adaptively adjusting the shape of the convolutional ker- nel to tackle
the substantial variability in cell morphology. 2) To overcome the issue
of unreasonable location maps, we propose the Pseudo-Scale Instance
(PSI) map, which can adaptively provide the corresponding scale
information for each cell to realize accurate supervision. We analyze
and evaluate DDConv and the PSI map in three challenging cell
localization tasks. In comparison to existing methods, our proposed
approach significantly enhances localization performance, setting a new
benchmark for the cell localiza- tion task.%
\end{abstract}%



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