无需参考图像，自动检测病理切片的染色与细胞质量缺陷
DPC-QA Net: A No-Reference Dual-Stream Perceptual and Cellular Quality Assessment Network for Histopathology Images
- 双流结构：全局感知+细胞层级嵌入，融合波尔特差异与核膜特征
- 在多个数据集上识别染色/膜/核问题准确率超92%，优于现有无参考模型
- 预测质量与细胞识别精度强相关，适合病理图像预筛场景
可靠全切片成像（WSI）依赖图像质量，但染色伪影、模糊和细胞退化常见。本文提出DPC-QA Net，一种无参考双流网络，结合基于小波的全局差异感知与通过Aggr-RWKV模块提取的核与膜嵌入进行细胞级质量评估。交叉注意力融合与多目标损失使感知与细胞线索对齐。在不同数据集上，模型对染色、膜、核问题的检测准确率均超过92%，且与可用性评分高度一致；在LIVEC和KonIQ上优于现有无参考图像质量评估（NR-IQA）模型。下游研究显示，预测质量与细胞识别精度显著正相关（如核分割的PQ/Dice、膜边界F-score），可实现计算病理中WSI区域的高效预筛选。
原文摘要 · Abstract (English)
Reliable whole slide imaging (WSI) hinges on image quality,yet staining artefacts, defocus, and cellular degradations are common. We present DPC-QA Net, a no-reference dual-stream network that couples wavelet-based global difference perception with cellular quality assessment from nuclear and membrane embeddings via an Aggr-RWKV module. Cross-attention fusion and multi-term losses align perceptual and cellular cues. Across different datasets, our model detects staining, membrane, and nuclear issues with >92% accuracy and aligns well with usability scores; on LIVEC and KonIQ it outperforms state-of-the-art NR-IQA. A downstream study further shows strong positive correlations between predicted quality and cell recognition accuracy (e.g., nuclei PQ/Dice, membrane boundary F-score), enabling practical pre-screening of WSI regions for computational pathology.
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