Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging.
Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging.
复制标题
DOI：
10.3390/cancers13040738
复制
发表时间：
2021-02-10
期刊：
影响因子：
5.2
通讯作者：
Choe SW
中科院分区：
文献类型：
--
作者：
Ayana G;Dese K;Choe SW
迁移学习在医学图像分析中发挥着重要作用;然而，为机器学习算法获得足够的训练图像数据集可能具有挑战性。虽然许多研究试图在医学图像分析中采用迁移学习，但到目前为止，只有少数关于迁移学习在医学图像分析中应用的综述文章已经发表。此外，关于转移学习在超声乳腺成像中的应用的综述很少。本文回顾了以往通过迁移学习从超声图像中检测乳腺癌的研究，以总结现有方法并确定其优点和缺点。此外，本文还提出了将迁移学习应用于超声成像以检测和诊断乳腺癌的潜在未来研究方向。这篇综述预计将有助于指导研究人员通过进一步研究基于迁移学习的超声乳腺成像来确定潜在的改进方法和可以改进的领域。迁移学习是一种机器学习方法，它重复使用为任务开发的学习方法作为目标任务模型的起点。迁移学习的目标是通过迁移包含在其他（但相关）源域中的知识来提高目标学习者的成绩。其结果是，需要大量的目标领域的数据被降低，以构建目标学习者。由于这种巨大的属性，迁移学习技术经常用于超声乳腺癌图像分析。在这篇综述中，我们从迁移学习方法、预处理、预训练模型和卷积神经网络（CNN）模型的角度重点介绍了应用于超声乳腺图像分类和检测的迁移学习方法。最后，不同的作品进行了比较，并讨论了挑战以及展望。
Transfer learning plays a major role in medical image analyses; however, obtaining adequate training image datasets for machine learning algorithms can be challenging. Although many studies have attempted to employ transfer learning in medical image analyses, thus far, only a few review articles regarding the application of transfer learning to medical image analyses have been published. Moreover, reviews on the application of transfer learning in ultrasound breast imaging are rare. This work reviews previous studies that focused on detecting breast cancer from ultrasound images by using transfer learning, in order to summarize existing methods and identify their advantages and shortcomings. Additionally, this review presents potential future research directions for applying transfer learning in ultrasound imaging for the purposes of breast cancer detection and diagnoses. This review is expected to be significantly helpful in guiding researchers to identify potential improved methods and areas that can be improved through further research on transfer learning-based ultrasound breast imaging. Transfer learning is a machine learning approach that reuses a learning method developed for a task as the starting point for a model on a target task. The goal of transfer learning is to improve performance of target learners by transferring the knowledge contained in other (but related) source domains. As a result, the need for large numbers of target-domain data is lowered for constructing target learners. Due to this immense property, transfer learning techniques are frequently used in ultrasound breast cancer image analyses. In this review, we focus on transfer learning methods applied on ultrasound breast image classification and detection from the perspective of transfer learning approaches, pre-processing, pre-training models, and convolutional neural network (CNN) models. Finally, comparison of different works is carried out, and challenges—as well as outlooks—are discussed.
登录
查看更多内容
影响因子：
8.1
作者：
Best, Natalie;Ott, Jordan;Linstead, Erik J.
通讯作者：
Linstead, Erik J.
DOI：
10.1007/978-3-030-11149-6_13
发表时间：
2019-01-01
期刊：
DISEASES OF THE CHEST, BREAST, HEART AND VESSELS 2019-2022: DIAGNOSTIC AND INTERVENTIONAL IMAGING
影响因子：
--
作者：
Gilbert, Fiona J.;Pinker-Domenig, Katja
通讯作者：
Pinker-Domenig, Katja
影响因子：
2.2
作者：
Coleman, Cathy
通讯作者：
Coleman, Cathy
影响因子：
10.9
作者：
Cheplygina, Veronika;de Bruijne, Marleen;Pluim, Josien P. W.
通讯作者：
Pluim, Josien P. W.
影响因子：
3.8
作者：
Drukker, K;Giger, ML;Mendelson, EB
通讯作者：
Mendelson, EB