Diagnosis of breast cancer malignancy at the early stages is a crucial step for controlling its side effects. Histopathological analysis provides a unique opportunity for malignant breast cancer detection. However, such a task would be tedious and time-consuming for the histopathologists. Deep Neural Networks enable us to learn informative features directly from raw histopathological images without manual feature extraction. Although Convolutional Neural Networks (CNNs) have been the dominant architectures in the computer vision realm, Transformer-based architectures have shown promising results in different computer vision tasks. Although harnessing the capability of Transformer-based architectures for medical image analysis seems interesting, these architectures are large, have a significant number of trainable parameters, and require large datasets to be trained on, which are usually rare in the medical domain. It has been claimed and empirically proved that at least part of the superior performance of Transformer-based architectures in Computer Vision domain originates from patch embedding operation. In this paper, we borrowed the previously introduced idea of integrating a fully Convolutional Neural Network architecture with Patch Embedding operation and presented an efficient CNN architecture for breast cancer malignancy detection from histopathological images. Despite the number of parameters that is significantly smaller than other methods, the accuracy performance metrics achieved 97.65%, 98.92%, 99.21%, and 98.01% for 40x, 100x, 200x, and 400x magnifications respectively. We took a step forward and modified the architecture using Group Convolution and Channel Shuffling ideas and reduced the number of trainable parameters even more with a negligible decline in performance and achieved 95.42%, 98.16%, 96.05%, and 97.92% accuracy for the mentioned magnifications respectively.

翻译：乳腺癌早期恶性诊断是控制其副作用的关键步骤。组织病理学分析为恶性乳腺癌检测提供了独特机会，但这类任务对病理学家而言繁琐且耗时。深度神经网络能够直接从原始组织病理图像中学习信息丰富的特征，无需手动特征提取。尽管卷积神经网络（CNN）长期以来是计算机视觉领域的主导架构，但基于Transformer的架构在不同计算机视觉任务中展现了优异结果。虽然将Transformer架构应用于医学图像分析颇具吸引力，但这些模型规模庞大、可训练参数众多，且需要大量数据集进行训练——这在医学领域通常较为稀缺。已有研究提出并实证验证，Transformer架构在计算机视觉领域的优越性能至少部分源于补丁嵌入操作。本文借鉴了将全卷积神经网络架构与补丁嵌入操作相结合的前期思想，提出了一种高效CNN架构用于组织病理图像的乳腺癌恶性检测。尽管参数量远小于其他方法，该模型在40倍、100倍、200倍和400倍放大倍数下的准确率指标分别达到97.65%、98.92%、99.21%和98.01%。我们进一步采用分组卷积与通道混洗思想改进架构，在性能几乎无下降的情况下进一步减少可训练参数，在相应放大倍数下分别实现95.42%、98.16%、96.05%和97.92%的准确率。