arXiv 2408.019292024-08-04eess.IVcs.CV

推进乳腺癌中 H&E 到 IHC 染色转换：一种基于多放大倍率和注意力的方法

Advancing H&E-to-IHC Stain Translation in Breast Cancer: A Multi-Magnification and Attention-Based Approach

Fudan University（复旦大学）；College of Electronic Engineering, Ocean University of China（中国海洋大学电子工程学院）；Research Center for Frontier Fundamental Studies, Zhejiang Lab（之江实验室前沿基础研究中心）；Institutes of Biomedical Sciences, Fudan University（复旦大学生物医学研究院）

Linhao Qu, Chengsheng Zhang, Guihui Li, Haiyong Zheng, Chen Peng, Wei He

备注：: Accepted by IEEE CIS-RAM 2024 Invited Session Oral

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AI总结：

针对从 H&E 合成 IHC-HER2 病理图像时的多放大倍率融合与关键信息关注不足问题，本文提出结合注意力机制和多倍率处理的生成模型，并在公开乳腺癌数据集上取得最先进性能。

摘要：

乳腺癌在全球范围内构成重大医疗保健挑战，需要精确诊断和有效治疗策略，而苏木精和伊红（H&E）染色组织切片的组织病理学检查在其中发挥核心作用。尽管其十分重要，但评估人表皮生长因子受体2（HER2）等特定生物标志物以进行个体化治疗，仍受到免疫组织化学（IHC）资源密集特性的限制。深度学习，尤其是图像到图像转换领域的最新进展，为从 H&E 染色切片合成 IHC-HER2 切片提供了前景。然而，现有方法面临诸多挑战，包括处理病理图像中的多种放大倍率，以及在转换过程中对关键信息的关注不足。为解决这些问题，我们提出了一种整合注意力机制和多放大倍率信息处理的新型模型。该模型采用多放大倍率处理策略，从病理图像内的不同放大倍率中提取并利用信息，从而实现稳健的图像转换。此外，生成网络中的注意力模块会优先处理用于图像分布转换的关键信息，同时尽量减少相关性较低的细节。在一个公开可用的乳腺癌数据集上进行的严格测试表明，与现有方法相比，该模型具有更优越的性能，确立了其作为推进从 H&E 到 IHC 染色的病理图像转换的最先进解决方案。

英文摘要：

Breast cancer presents a significant healthcare challenge globally, demanding precise diagnostics and effective treatment strategies, where histopathological examination of Hematoxylin and Eosin (H&E) stained tissue sections plays a central role. Despite its importance, evaluating specific biomarkers like Human Epidermal Growth Factor Receptor 2 (HER2) for personalized treatment remains constrained by the resource-intensive nature of Immunohistochemistry (IHC). Recent strides in deep learning, particularly in image-to-image translation, offer promise in synthesizing IHC-HER2 slides from H\&E stained slides. However, existing methodologies encounter challenges, including managing multiple magnifications in pathology images and insufficient focus on crucial information during translation. To address these issues, we propose a novel model integrating attention mechanisms and multi-magnification information processing. Our model employs a multi-magnification processing strategy to extract and utilize information from various magnifications within pathology images, facilitating robust image translation. Additionally, an attention module within the generative network prioritizes critical information for image distribution translation while minimizing less pertinent details. Rigorous testing on a publicly available breast cancer dataset demonstrates superior performance compared to existing methods, establishing our model as a state-of-the-art solution in advancing pathology image translation from H&E to IHC staining.

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