用神经元胞自动机实现弱监督白血细胞分割，无需标注也能精准定位。
Neural Cellular Automata for Weakly Supervised Segmentation of White Blood Cells
- 利用神经元胞自动机的特征图生成分割掩码，免去重新标注训练。
- 在三个数据集上性能显著优于现有弱监督方法。
- 适合医疗影像中缺乏精细标注但需高精度分割的场景。
血涂片中白血细胞的检测与分割是医学诊断的关键步骤，支持自动化计数、形态分析、分类及疾病诊断与监测。训练鲁棒准确的模型需要大量标注数据，而获取这些数据既耗时又昂贵。本文提出一种基于神经元胞自动机的弱监督分割方法（NCA-WSS）。通过利用NCA在分类过程中生成的特征图，可直接提取分割掩码，无需额外的标注和重训练。我们在三个白血细胞显微图像数据集上评估该方法，结果表明其显著优于现有弱监督分割技术。本工作展示了NCA在弱监督框架下同时实现分类与分割的潜力，为医学图像分析提供了可扩展、高效的解决方案。
原文摘要 · Abstract (English)
The detection and segmentation of white blood cells in blood smear images is a key step in medical diagnostics, supporting various downstream tasks such as automated blood cell counting, morphological analysis, cell classification, and disease diagnosis and monitoring. Training robust and accurate models requires large amounts of labeled data, which is both time-consuming and expensive to acquire. In this work, we propose a novel approach for weakly supervised segmentation using neural cellular automata (NCA-WSS). By leveraging the feature maps generated by NCA during classification, we can extract segmentation masks without the need for retraining with segmentation labels. We evaluate our method on three white blood cell microscopy datasets and demonstrate that NCA-WSS significantly outperforms existing weakly supervised approaches. Our work illustrates the potential of NCA for both classification and segmentation in a weakly supervised framework, providing a scalable and efficient solution for medical image analysis.
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