Abstract
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.
Abstract (translated)
在血涂片图像中检测和分割白细胞是医学诊断中的关键步骤，支持自动血液计数、形态分析、细胞分类以及疾病诊断与监测等下游任务。训练稳健且准确的模型需要大量的标注数据，而这既耗时又昂贵。为此，我们提出了一种基于神经元细胞自动机（Neural Cellular Automata, NCA）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，我们可以无需使用分割标签重新训练模型即可提取出分割掩模。我们在三个白血细胞显微镜数据集上评估了该方法，并证明NCA-WSS显著优于现有的弱监督方法。我们的研究展示了NCA在弱监督框架下同时用于分类和分割任务的巨大潜力，为医学图像分析提供了一种可扩展且高效的解决方案。
URL
https://arxiv.org/abs/2508.12322
