计算机科学
稳健性(进化)
水准点(测量)
比例(比率)
核(代数)
卷积(计算机科学)
人工智能
编码(集合论)
计算机视觉
模式识别(心理学)
数学
地图学
生物化学
化学
集合(抽象数据类型)
组合数学
人工神经网络
基因
程序设计语言
地理
作者
Chengyang Zhang,Jie Chen,Bo Li,Min Feng,Yongquan Yang,Qikui Zhu,Hong Bu
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:28 (1): 355-366
被引量:2
标识
DOI:10.1109/jbhi.2023.3329542
摘要
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 robustness to color by leveraging gradient information while adaptively adjusting the shape of the convolutional kernel 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 localization task. Our code is available at https://github.com/ChyaZhang/DDConv-PSI.
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