解决密集细胞计数难题，提升准确率超40%
DLA-Count: Dynamic Label Assignment Network for Dense Cell Distribution Counting
- 采用动态标签分配机制优化密集区域细胞匹配
- 在四个数据集上误差降低最高达46.7%
- 适合医学图像中复杂形态细胞的精准计数
细胞计数在医学与生物学研究中仍是基础且具挑战性的任务，原因在于细胞形态多样、分布密集以及图像质量差异。本文提出DLA-Count，引入三项创新：(1) K-邻近匈牙利匹配（KHM），显著提升密集区域细胞匹配精度；(2) 多尺度可变形高斯卷积（MDGC），适应不同细胞形态；(3) 高斯增强特征解码器（GFD），实现高效多尺度特征融合。在四个具有挑战性的细胞计数数据集（ADI、MBM、VGG、DCC）上的大量实验表明，该方法在多个数据集上均优于现有方法，尤其在ADI数据集上平均绝对误差降低46.7%，在MBM数据集上降低42.5%。代码已公开于https://anonymous.4open.science/r/DLA-Count。
原文摘要 · Abstract (English)
Cell counting remains a fundamental yet challenging task in medical and biological research due to the diverse morphology of cells, their dense distribution, and variations in image quality. We present DLA-Count, a breakthrough approach to cell counting that introduces three key innovations: (1) K-adjacent Hungarian Matching (KHM), which dramatically improves cell matching in dense regions, (2) Multi-scale Deformable Gaussian Convolution (MDGC), which adapts to varying cell morphologies, and (3) Gaussian-enhanced Feature Decoder (GFD) for efficient multi-scale feature fusion. Our extensive experiments on four challenging cell counting datasets (ADI, MBM, VGG, and DCC) demonstrate that our method outperforms previous methods across diverse datasets, with improvements in Mean Absolute Error of up to 46.7\% on ADI and 42.5\% on MBM datasets. Our code is available at https://anonymous.4open.science/r/DLA-Count.
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