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Abstract：
Breast cancer diagnosis from histopathology images is often time consuming and prone to human error, impacting treatment and prognosis. Deep learning diagnostic methods offer the potential for improved accuracy and efficiency in breast cancer detection and classification. However, they struggle with limited data and subtle variations within and between cancer types. Attention mechanisms provide feature refinement capabilities that have shown promise in overcoming such challenges. To this end, this paper proposes the Efficient Channel Spatial Attention Network (ECSAnet), an architecture built on EfficientNetV2 and augmented with a convolutional block attention module (CBAM) and additional fully connected layers. ECSAnet was fine-tuned using the BreakHis dataset, employing Reinhard stain normalization and image augmentation techniques to minimize overfitting and enhance generalizability. In testing, ECSAnet outperformed AlexNet, DenseNet121, EfficientNetV2-S, InceptionNetV3, ResNet50, and VGG16 in most settings, achieving accuracies of 94.2% at 40×, 92.96% at 100×, 88.41% at 200×, and 89.42% at 400× magnifications. The results highlight the effectiveness of CBAM in improving classification accuracy and the importance of stain normalization for generalizability.
摘要：
从组织病理学图像诊断乳腺癌通常是耗时的，并且容易出现人为错误。影响治疗和预后。深度学习诊断方法为提高乳腺癌检测和分类的准确性和效率提供了潜力。然而,他们在有限的数据和癌症类型之间的微妙变化中挣扎。注意机制提供了在克服此类挑战方面显示出希望的特征细化能力。为此,本文提出了高效信道空间注意力网络(ECSAnet)，基于EfficientNetV2构建的架构，并使用卷积块注意力模块（CBAM）和其他完全连接的层进行增强。ECSAnet使用BreakHis数据集进行了微调，采用Reinhardstain归一化和图像增强技术，以最大程度地减少过拟合并增强泛化性。在测试中,ECSAnet的表现优于AlexNet，DenseNet121,EfficientNetV2-S,在大多数设置中，InceptionNetV3、ResNet50和VGG16，在40×时达到94.2%的精度，100×时92.96%，200×88.41%,400倍放大倍数为89.42%。结果强调了CBAM在提高分类准确性方面的有效性以及染色标准化对可泛化性的重要性。
