深度学习方法在乳腺癌医学图像诊断中的研究进展
Research Progress of Deep Learning Methods in the Diagnosis of Breast Cancer Medical Images
DOI: 10.12677/airr.2025.146123, PDF,    科研立项经费支持
作者: 崔文升, 李 欣：大庆师范学院计算机科学与信息技术学院，黑龙江 大庆
关键词: 深度学习；乳腺癌；医学图像；Deep Learning； Breast Cancer； Medical Image
摘要: 乳腺癌是全球女性中最常见的恶性肿瘤之一，早期且准确的诊断对提升患者生存率至关重要。近年来，深度学习凭借在特征提取、分类、分割与风险预测等任务中的优越表现，被广泛应用于医学图像分析。然而，现有综述多聚焦于单一影像模态或技术视角，缺乏对整体研究进展的系统梳理。为弥补这一不足，本文系统介绍了三类主流乳腺癌数据集，简介了基于深度学习方法的研究成果，并归纳了不同影像模态的特点。最后，探讨了深度学习在乳腺癌医学图像诊断中的面临挑战和未来的发展趋势。本文旨在为学者全面理解该领域的最新进展与创新提供有益参考。
Abstract: Breast cancer remains one of the most frequently diagnosed malignancies among women worldwide, and early and accurate diagnosis is crucial for enhancing patient survival outcomes. In recent years, deep learning has been increasingly utilized in the field of medical image analysis due to its outstanding capabilities in feature extraction, classification, segmentation, and risk prediction. However, the majority of existing reviews concentrate on a single imaging modality or a specific technical framework, resulting in a lack of comprehensive and systematic insights into recent advancements in this area. To address this limitation, this paper presents a systematic review of three major breast cancer datasets, outlines recent research achievements enabled by deep learning techniques, and elucidates the distinctive features of various imaging modalities. Additionally, it explores current challenges and proposes potential future directions for the application of deep learning in breast cancer image diagnosis. This review aims to serve as a valuable reference for researchers seeking a thorough understanding of the latest developments and innovations in this rapidly advancing field.
文章引用：崔文升, 李欣. 深度学习方法在乳腺癌医学图像诊断中的研究进展[J]. 人工智能与机器人研究, 2025, 14(6): 1314-1326. https://doi.org/10.12677/airr.2025.146123

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