Breast cancer is one of the most common types of cancer and is the leading cause of cancer-related death. Diagnosis of breast cancer is based on the evaluation of pathology slides. In the era of digital pathology, these slides can be converted into digital whole slide images (WSIs) for further analysis. However, due to their sheer size, digital WSIs diagnoses are time consuming and challenging. In this study, we present a lightweight architecture that consists of a bilinear structure and MobileNet-V3 network, bilinear MobileNet-V3 (BM-Net), to analyze breast cancer WSIs. We utilized the WSI dataset from the ICIAR2018 Grand Challenge on Breast Cancer Histology Images (BACH) competition, which contains four classes: normal, benign, in situ carcinoma, and invasive carcinoma. We adopted data augmentation techniques to increase diversity and utilized focal loss to remove class imbalance. We achieved high performance, with 0.88 accuracy in patch classification and an average 0.71 score, which surpassed state-of-the-art models. Our BM-Net shows great potential in detecting cancer in WSIs and is a promising clinical tool.

中文翻译：

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BM-Net：基于 CNN 的 MobileNet-V3 和双线性结构，用于在整个幻灯片图像中检测乳腺癌

乳腺癌是最常见的癌症类型之一，也是癌症相关死亡的主要原因。乳腺癌的诊断基于病理切片的评估。在数字病理学时代，这些载玻片可以转换为数字全载玻片图像 (WSI) 以供进一步分析。然而，由于其庞大的规模，数字 WSI 诊断既耗时又具有挑战性。在这项研究中，我们提出了一种由双线性结构和 MobileNet-V3 网络组成的轻量级架构，即双线性 MobileNet-V3 (BM-Net)，用于分析乳腺癌 WSI。我们利用了 ICIAR2018 乳腺癌组织学图像大挑战赛 (BACH) 竞赛中的 WSI 数据集，该数据集包含四个类别：正常、良性、原位癌和浸润性癌。我们采用数据增强技术来增加多样性，并利用焦点损失来消除类别不平衡。我们实现了高性能，补丁分类准确率为 0.88，平均得分为 0.71，超过了最先进的模型。我们的 BM-Net 在检测 WSI 中的癌症方面显示出巨大的潜力，是一种很有前途的临床工具。