文章：

H&E染色归一化对深度学习模型在癌症图像分类中的影响：性能、复杂度与权衡

Impact of H&E Stain Normalization on Deep Learning Models in Cancer Image Classification: Performance, Complexity, and Trade-Offs

原文发布日期：17 August 2023

DOI: 10.3390/cancers15164144

类型: Article

开放获取: 是

英文摘要：

Accurate classification of cancer images plays a crucial role in diagnosis and treatment planning. Deep learning (DL) models have shown promise in achieving high accuracy, but their performance can be influenced by variations in Hematoxylin and Eosin (H&E) staining techniques. In this study, we investigate the impact of H&E stain normalization on the performance of DL models in cancer image classification. We evaluate the performance of VGG19, VGG16, ResNet50, MobileNet, Xception, and InceptionV3 on a dataset of H&E-stained cancer images. Our findings reveal that while VGG16 exhibits strong performance, VGG19 and ResNet50 demonstrate limitations in this context. Notably, stain normalization techniques significantly improve the performance of less complex models such as MobileNet and Xception. These models emerge as competitive alternatives with lower computational complexity and resource requirements and high computational efficiency. The results highlight the importance of optimizing less complex models through stain normalization to achieve accurate and reliable cancer image classification. This research holds tremendous potential for advancing the development of computationally efficient cancer classification systems, ultimately benefiting cancer diagnosis and treatment.

摘要翻译： 

癌症图像的准确分类在诊断与治疗规划中起着至关重要的作用。深度学习模型虽已展现出实现高准确率的潜力，但其性能可能受苏木精-伊红染色技术差异的影响。本研究探讨了H&E染色标准化对深度学习模型在癌症图像分类中性能的影响。我们在H&E染色癌症图像数据集上评估了VGG19、VGG16、ResNet50、MobileNet、Xception和InceptionV3模型的性能。研究发现，虽然VGG16表现出色，但VGG19和ResNet50在此场景中存在局限性。值得注意的是，染色标准化技术显著提升了MobileNet和Xception等复杂度较低模型的性能。这些模型凭借较低的计算复杂度、资源需求及较高的计算效率，成为具有竞争力的替代方案。研究结果凸显了通过染色标准化优化复杂度较低模型以实现准确可靠癌症图像分类的重要性。该研究对推动计算高效的癌症分类系统发展具有巨大潜力，最终将惠及癌症诊断与治疗。

原文链接：

Impact of H&E Stain Normalization on Deep Learning Models in Cancer Image Classification: Performance, Complexity, and Trade-Offs