用风格对齐技术合成病理图像，提升异常细胞检测效果。
Style-Aligned Image Composition for Robust Detection of Abnormal Cells in Cytopathology
- 根据属性引导选择异常细胞，通过高频特征重建实现风格一致的合成
- 合成图像使小类和不同染色风格下的检测性能显著提升
- 适合需要增强数据多样性的病理检测研究者使用
缺乏高质量标注、长尾数据分布不均以及染色风格不一致，严重制约了神经网络在细胞病理学中稳健检测异常细胞的能力。本文提出风格对齐图像合成（SAIC）方法，生成高保真且风格保留的病理图像，以增强检测模型的有效性与鲁棒性。无需额外训练，SAIC首先基于属性引导从异常细胞库中选取合适候选样本；随后利用高频特征重建实现异常细胞与病理背景的风格对齐与高保真融合；最后借助大视觉-语言模型筛选高质量合成图像。实验表明，引入SAIC合成图像显著提升了小类别及不同染色风格下的异常细胞检测性能，整体检测效果得到改善。全面的质量评估进一步验证了SAIC在临床场景中的泛化能力与实用性。代码将发布于https://github.com/Joey-Qi/SAIC。
原文摘要 · Abstract (English)
Challenges such as the lack of high-quality annotations, long-tailed data distributions, and inconsistent staining styles pose significant obstacles to training neural networks to detect abnormal cells in cytopathology robustly. This paper proposes a style-aligned image composition (SAIC) method that composes high-fidelity and style-preserved pathological images to enhance the effectiveness and robustness of detection models. Without additional training, SAIC first selects an appropriate candidate from the abnormal cell bank based on attribute guidance. Then, it employs a high-frequency feature reconstruction to achieve a style-aligned and high-fidelity composition of abnormal cells and pathological backgrounds. Finally, it introduces a large vision-language model to filter high-quality synthesis images. Experimental results demonstrate that incorporating SAIC-synthesized images effectively enhances the performance and robustness of abnormal cell detection for tail categories and styles, thereby improving overall detection performance. The comprehensive quality evaluation further confirms the generalizability and practicality of SAIC in clinical application scenarios. Our code will be released at https://github.com/Joey-Qi/SAIC.
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