SCFANet：用于病理染色转换的风格分布约束特征对齐网络
SCFANet: Style Distribution Constraint Feature Alignment Network For Pathological Staining Translation
AI总结：
本文提出SCFANet网络，通过风格分布约束器和特征对齐学习模块，结合病理模式与光密度一致性，有效解决H&E到IHC图像转换中的对齐与风格差异挑战，在BCI数据集上表现优于现有方法。
摘要：
免疫组织化学（IHC）染色是一种通过抗体介导可视化来检测特定抗原或蛋白质的重要技术。然而，IHC染色过程既耗时又昂贵。为了解决这些局限性，应用深度学习模型将具有成本效益的苏木精和伊红（H&E）染色图像直接转换为IHC染色图像已成为一种高效的解决方案。尽管如此，从H&E到IHC图像的转换面临重大挑战，主要原因是图像对之间的对齐差异以及IHC染色风格模式的内在多样性。为了克服这些挑战，我们提出了风格分布约束特征对齐网络（SCFANet），该网络包含两个创新模块：风格分布约束器（SDC）和特征对齐学习（FAL）。SDC确保生成图像与目标图像之间风格分布的一致性，同时整合循环一致性损失以维持结构一致性。为了减轻直接图像到图像转换的复杂性，FAL模块将端到端转换任务分解为两个子任务：图像重建和特征对齐。此外，我们通过保持病理模式的一致性和光密度（OD）的均匀性，确保生成图像与目标图像之间的病理一致性。在乳腺癌免疫组织化学（BCI）数据集上进行的大量实验表明，我们的SCFANet模型优于现有方法，实现了H&E染色图像向IHC染色图像的精确转换。所提出的方法不仅解决了H&E到IHC图像转换的技术挑战，还为病理分析中准确高效的染色转换提供了稳健的框架。
英文摘要：
Immunohistochemical (IHC) staining serves as a valuable technique for detecting specific antigens or proteins through antibody-mediated visualization. However, the IHC staining process is both time-consuming and costly. To address these limitations, the application of deep learning models for direct translation of cost-effective Hematoxylin and Eosin (H&E) stained images into IHC stained images has emerged as an efficient solution. Nevertheless, the conversion from H&E to IHC images presents significant challenges, primarily due to alignment discrepancies between image pairs and the inherent diversity in IHC staining style patterns. To overcome these challenges, we propose the Style Distribution Constraint Feature Alignment Network (SCFANet), which incorporates two innovative modules: the Style Distribution Constrainer (SDC) and Feature Alignment Learning (FAL). The SDC ensures consistency between the generated and target images' style distributions while integrating cycle consistency loss to maintain structural consistency. To mitigate the complexity of direct image-to-image translation, the FAL module decomposes the end-to-end translation task into two subtasks: image reconstruction and feature alignment. Furthermore, we ensure pathological consistency between generated and target images by maintaining pathological pattern consistency and Optical Density (OD) uniformity. Extensive experiments conducted on the Breast Cancer Immunohistochemical (BCI) dataset demonstrate that our SCFANet model outperforms existing methods, achieving precise transformation of H&E-stained images into their IHC-stained counterparts. The proposed approach not only addresses the technical challenges in H&E to IHC image translation but also provides a robust framework for accurate and efficient stain conversion in pathological analysis.
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