Breast cancer(BC) is a prevalent type of malignant tumor in women. Early diagnosis and treatment are vital for enhancing the patients' survival rate. Downsampling in deep networks may lead to loss of information, so for compensating the detail and edge information and allowing convolutional neural networks to pay more attention to seek the lesion region, we propose a multi-stages attention architecture based on NSNP neurons with autapses. First, unlike the single-scale attention acquisition methods of existing methods, we set up spatial attention acquisition at each feature map scale of the convolutional network to obtain an fusion global information on attention guidance. Then we introduce a new type of NSNP variants called NSNP neurons with autapses. Specifically, NSNP systems are modularized as feature encoders, recoding the features extracted from convolutional neural network as well as the fusion of attention information and preserve the key characteristic elements in feature maps. This ensures the retention of valuable data while gradually transforming high-dimensional complicated info into low-dimensional ones. The proposed method is evaluated on the public dataset BreakHis at various magnifications and classification tasks. It achieves a classification accuracy of 96.32% at all magnification cases, outperforming state-of-the-art methods. Ablation studies are also performed, verifying the proposed model's efficacy. The source code is available at XhuBobYoung/Breast-cancer-Classification.

翻译：乳腺癌（BC）是女性中常见的恶性肿瘤类型。早期诊断和治疗对于提高患者的生存率至关重要。深度网络中的下采样可能导致信息丢失，为补偿细节和边缘信息，并使卷积神经网络更专注于病变区域的搜寻，我们提出了一种基于带有自突触的NSNP神经元的多阶段注意力架构。首先，与现有方法单尺度注意力获取方式不同，我们在卷积网络每个特征图尺度上设置空间注意力获取，以获得融合全局信息的注意力引导。随后，我们引入了一种新型NSNP变体——带有自突触的NSNP神经元。具体来说，将NSNP系统模块化为特征编码器，对卷积神经网络提取的特征以及注意力信息的融合进行重新编码，并保留特征图中的关键特征元素。这确保了在将高维复杂信息逐步转换为低维信息的同时保留有价值的数据。所提出的方法在公开数据集BreakHis上，针对不同放大倍率和分类任务进行了评估。在所有放大倍率情况下，分类准确率达到96.32%，优于现有最先进方法。同时进行了消融研究，验证了所提模型的有效性。源代码可在XhuBobYoung/Breast-cancer-Classification获取。