Jiang Liang, Zhang Cheng, Cao Hui, Jiang Baihao
College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Oct 25;41(5):1072-1077. doi: 10.7507/1001-5515.202311061.
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.
乳腺癌是一种由乳腺上皮细胞异常增殖引起的恶性肿瘤，主要影响女性患者，通常通过组织病理学图像进行诊断。目前，深度学习技术在医学图像处理方面取得了重大突破，在乳腺癌病理分类任务中优于传统检测方法。本文首先回顾了深度学习在乳腺病理图像应用方面的进展，重点关注三个关键领域：多尺度特征提取、细胞特征分析和分类。接下来，总结了乳腺病理图像多模态数据融合方法的优势。最后，该研究讨论了深度学习在乳腺癌病理图像诊断中的挑战和未来前景，为推动深度学习在乳腺诊断中的应用提供了重要指导。
