[Research progress of breast pathology image diagnosis based on deep learning].

Liang Jiang, Cheng Zhang, Hui Cao, Baihao Jiang
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Abstract

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.

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[基于深度学习的乳腺病理图像诊断研究进展]。
乳腺癌是一种由乳腺上皮细胞异常增生引起的恶性肿瘤,主要影响女性患者,通常使用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领域取得了重大突破,在乳腺癌病理分类任务中的表现优于传统检测方法。本文首先回顾了将深度学习应用于乳腺病理图像的进展,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。接着,它总结了乳腺病理图像多模态数据融合方法的优势。最后,研究探讨了深度学习在乳腺癌病理图像诊断中面临的挑战和未来前景,为推进深度学习在乳腺诊断中的应用提供了重要指导。
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来源期刊
生物医学工程学杂志
生物医学工程学杂志 Medicine-Medicine (all)
CiteScore
0.80
自引率
0.00%
发文量
4868
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