Breast cancer is a malignancy caused by the abnormal proliferation of breast epithelial cells, predominantly affecting female patients, and it is commonly diagnosed using histopathological images. Currently, deep learning techniques have made significant breakthroughs in medical image processing, outperforming traditional detection methods in breast cancer pathology classification tasks. This paper first reviewed the advances in applying deep learning to breast pathology images, focusing on three key areas: multi-scale feature extraction, cellular feature analysis, and classification. Next, it summarized the advantages of multimodal data fusion methods for breast pathology images. Finally, the study discussed the challenges and future prospects of deep learning in breast cancer pathology image diagnosis, providing important guidance for advancing the use of deep learning in breast diagnosis.
乳腺癌是由于乳腺上皮细胞异常增殖所导致的恶性疾病，多见于女性患者，临床上常用乳腺癌组织病理图像进行诊断。现阶段深度学习技术在医学图像处理领域取得突破性进展，在乳腺癌病理分类任务中效果优于传统检测技术。本文首先阐述了深度学习在乳腺病理图像的应用进展，从多尺度特征提取、细胞特征分析以及分类分型三个方面进行了概述，其次归纳总结了多模态数据融合方法在乳腺病理图像上的优势，最后指出深度学习在乳腺癌病理图像诊断领域面临的挑战并展望未来，这对推进深度学习技术在乳腺诊断中的发展具有重要的指导意义。.
Keywords: Breast pathology images; Convolutional neural network; Deep learning; Multimodal data.