自动为血细胞图像标注多种形态属性，提升分析效率与模型可解释性。
AttriGen: Automated Multi-Attribute Annotation for Blood Cell Datasets
- 融合CNN与ViT双模型，实现细胞类型与多属性联合分类。
- 在WBCAtt数据集上达到94.62%的多属性分类准确率，创新高。
- 显著减少人工标注成本，适用于医学图像精细化分析场景。
我们提出AttriGen，一种面向计算机视觉的自动化细粒度多属性标注框架，特别针对细胞显微图像中多属性分类仍远落后于传统细胞类型分类的问题。基于两个互补数据集：包含八种细胞类型的外周血细胞（PBC）数据集，以及对应11个形态属性的白细胞属性数据集（WBCAtt），我们设计了一种双模型架构，结合卷积神经网络（CNN）进行细胞类型分类，以及视觉变换器（ViT）进行多属性分类，在新基准上达到94.62%的准确率。实验表明，AttriGen显著提升模型可解释性，并相比传统全人工标注大幅降低时间和成本。该框架为其他计算机视觉分类任务提供可扩展的多属性标签自动化方案。
原文摘要 · Abstract (English)
We introduce AttriGen, a novel framework for automated, fine-grained multi-attribute annotation in computer vision, with a particular focus on cell microscopy where multi-attribute classification remains underrepresented compared to traditional cell type categorization. Using two complementary datasets: the Peripheral Blood Cell (PBC) dataset containing eight distinct cell types and the WBC Attribute Dataset (WBCAtt) that contains their corresponding 11 morphological attributes, we propose a dual-model architecture that combines a CNN for cell type classification, as well as a Vision Transformer (ViT) for multi-attribute classification achieving a new benchmark of 94.62\% accuracy. Our experiments demonstrate that AttriGen significantly enhances model interpretability and offers substantial time and cost efficiency relative to conventional full-scale human annotation. Thus, our framework establishes a new paradigm that can be extended to other computer vision classification tasks by effectively automating the expansion of multi-attribute labels.
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