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.

通过引入基于 NSNP 神经元的多阶段注意架构，缓解了深度网络的降采样对信息的丢失，使卷积神经网络能够更多地关注病变区域，从而实现乳腺癌的早期诊断和治疗，进而提高患者的生存率。该方法在公共数据集 BreakHis 上进行了评估，以不同放大倍率和分类任务实现了 96.32% 的分类准确率，超越了现有方法的性能表现。还进行了剔除实验，验证了提出模型的有效性。