Deep learning advances in breast medical imaging with a focus on clinical readiness and radiologists’ perspective

IF 4.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Image and Vision Computing Pub Date : 2025-06-12 DOI:10.1016/j.imavis.2025.105601

Oladosu Oyebisi Oladimeji , Abdullah Al-Zubaer Imran , Xiaoqin Wang , Saritha Unnikrishnan

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Abstract

Breast cancer is the leading cause of death from cancer among women globally. According to the World Health Organization (WHO), early detection and treatment can significantly reduce surgeries and improve survival rates. Since deep learning emerged in 2012, it has garnered significant research interest in breast cancer, particularly for diagnosis, treatment, prognosis, and survival prediction. This review specifically focuses on the application of deep learning to breast image analysis (MRI, mammogram, and ultrasound) with a particular emphasis on radiologist involvement in the evaluation process. Studies published between 2019 and 2024 in the Scopus database will be reviewed. We further explore radiologists’ perspectives on the clinical readiness of artificial intelligence (AI) for breast image analysis. By analyzing insights from published articles, we will discuss the challenges, limitations, and future directions for this evolving field. While the review highlights the promise of deep learning in breast image analysis, it also acknowledges critical issues that must be addressed before widespread clinical integration can be achieved.

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深度学习在乳房医学成像方面的进步，重点是临床准备和放射科医生的观点

乳腺癌是全球妇女癌症死亡的主要原因。根据世界卫生组织（世卫组织）的说法，早期发现和治疗可以大大减少手术，提高生存率。自2012年深度学习出现以来，它在乳腺癌的诊断、治疗、预后和生存预测方面引起了极大的研究兴趣。这篇综述特别关注深度学习在乳房图像分析（MRI，乳房x光检查和超声波）中的应用，特别强调放射科医生在评估过程中的参与。将审查2019年至2024年在Scopus数据库中发表的研究。我们进一步探讨放射科医生对人工智能（AI）用于乳房图像分析的临床准备情况的看法。通过分析已发表文章的见解，我们将讨论这个不断发展的领域的挑战、限制和未来方向。虽然该综述强调了深度学习在乳房图像分析中的前景，但它也承认在实现广泛的临床整合之前必须解决的关键问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Image and Vision Computing 工程技术-工程：电子与电气

CiteScore

8.50

自引率

8.50%

发文量

143

审稿时长

7.8 months

期刊介绍： Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.