Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis.
Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis.
复制标题
作者：
乳腺癌是女性癌症死亡的主要原因之一。超声是一种无害的成像手段，用于帮助决定谁应该接受活检和乳腺癌治疗的几个方面。由于高度的操作员依赖性，它显示出高的假阳性，并且有可能使整体乳腺肿块管理具有成本效益。深度学习是人工智能的一种变体，在资源有限的情况下，对于减少超声操作员的工作量可能非常有用。这些深度学习模型已经在乳腺肿块诊断的各个方面进行了测试，但对于它们对诊断之外的影响以及主要使用了哪些超声方法，还没有足够的研究。本文综述了用于乳腺癌治疗的各种深度学习模型的研究现状，包括局限性和进一步研究的方向。乳腺癌是全世界女性死亡的第二大原因。超声是用于诊断乳腺病变和监测癌症患者预后的非侵入性成像手段之一。它对诊断乳腺肿块具有最高的敏感性，但由于其对操作员的高度依赖，它显示出更高的假阴性。服务不足的地区没有足够的美国专业知识来诊断乳房病变，导致乳房病变的处理延迟。深度学习神经网络通过快速而准确地诊断和监测患者的预后，可能有可能促进医生的早期决策。本文综述了神经网络在乳腺肿块超声诊断中的最新研究动态，包括诊断外的研究。我们讨论了最近进行的原始研究，以分析哪些超声波模式和哪些模式已用于哪些目的，以及它们在哪里显示出最佳性能。我们的分析表明，与用于其他目的的分类相比，病变分类显示出最高的性能。我们还发现，对预后的研究比对诊断的研究要少。我们还讨论了正在进行的乳腺超声神经网络研究的局限性和未来的方向。
Breast cancer is one of the leading causes of cancer death among women. Ultrasound is a harmless imaging modality used to help make decisions about who should undergo biopsies and several aspects of breast cancer management. It shows high false positivity due to high operator dependency and has the potential to make overall breast mass management cost-effective. Deep learning, a variant of artificial intelligence, may be very useful to reduce the workload of ultrasound operators in resource-limited settings. These deep learning models have been tested for various aspects of the diagnosis of breast masses, but there is not enough research on their impact beyond diagnosis and which methods of ultrasound have been mostly used. This article reviews current trends in research on various deep learning models for breast cancer management, including limitations and future directions for further research. Breast cancer is the second-leading cause of mortality among women around the world. Ultrasound (US) is one of the noninvasive imaging modalities used to diagnose breast lesions and monitor the prognosis of cancer patients. It has the highest sensitivity for diagnosing breast masses, but it shows increased false negativity due to its high operator dependency. Underserved areas do not have sufficient US expertise to diagnose breast lesions, resulting in delayed management of breast lesions. Deep learning neural networks may have the potential to facilitate early decision-making by physicians by rapidly yet accurately diagnosing and monitoring their prognosis. This article reviews the recent research trends on neural networks for breast mass ultrasound, including and beyond diagnosis. We discussed original research recently conducted to analyze which modes of ultrasound and which models have been used for which purposes, and where they show the best performance. Our analysis reveals that lesion classification showed the highest performance compared to those used for other purposes. We also found that fewer studies were performed for prognosis than diagnosis. We also discussed the limitations and future directions of ongoing research on neural networks for breast ultrasound.
登录
查看更多内容
影响因子：
--
作者：
Ashokkumar, N.;Meera, S.;Anandan, P.;Murthy, Mantripragada Yaswanth Bhanu;Kalaivani, K. S.;Alahmadi, Tahani Awad;Alharbi, Sulaiman Ali;Raghavan, S. S.;Jayadhas, S. Arockia
通讯作者：
Jayadhas, S. Arockia
影响因子：
3.4
作者：
Chang, Martin C.;Crystal, Pavel;Colgan, Terence J.
通讯作者：
Colgan, Terence J.
影响因子：
3.7
作者：
Boughey JC;Moriarty JP;Degnim AC;Gregg MS;Egginton JS;Long KH
通讯作者：
Long KH
影响因子：
5.2
作者：
Ayana G;Dese K;Choe SW
通讯作者：
Choe SW
影响因子：
3.3
作者：
Bitencourt A;Daimiel Naranjo I;Lo Gullo R;Rossi Saccarelli C;Pinker K
通讯作者：
Pinker K