Interactive Echocardiography Translation Using Few-Shot GAN Transfer Learning
المؤلفون المشاركون
Teng, Long
Fu, ZhongLiang
Ma, Qian
Yao, Yu
Zhang, Bing
Zhu, Kai
Li, Ping
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-03-19
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Background.
Interactive echocardiography translation is an efficient educational function to master cardiac anatomy.
It strengthens the student’s understanding by pixel-level translation between echocardiography and theoretically sketch images.
Previous research studies split it into two aspects of image segmentation and synthesis.
This split makes it hard to achieve pixel-level corresponding translation.
Besides, it is also challenging to leverage deep-learning-based methods in each phase where a handful of annotations are available.
Methods.
To address interactive translation with limited annotations, we present a two-step transfer learning approach.
Firstly, we train two independent parent networks, the ultrasound to sketch (U2S) parent network and the sketch to ultrasound (S2U) parent network.
U2S translation is similar to a segmentation task with sector boundary inference.
Therefore, the U2S parent network is trained with the U-Net network on the public segmentation dataset of VOC2012.
S2U aims at recovering ultrasound texture.
So, the S2U parent network is decoder networks that generate ultrasound data from random input.
After pretraining the parent networks, an encoder network is attached to the S2U parent network to translate ultrasound images into sketch images.
We jointly transfer learning U2S and S2U within the CGAN framework.
Results and conclusion.
Quantitative and qualitative contrast from 1-shot, 5-shot, and 10-shot transfer learning show the effectiveness of the proposed algorithm.
The interactive translation is achieved with few-shot transfer learning.
Thus, the development of new applications from scratch is accelerated.
Our few-shot transfer learning has great potential in the biomedical computer-aided image translation field, where annotation data are extremely precious.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Teng, Long& Fu, ZhongLiang& Ma, Qian& Yao, Yu& Zhang, Bing& Zhu, Kai…[et al.]. 2020. Interactive Echocardiography Translation Using Few-Shot GAN Transfer Learning. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1139335
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Teng, Long…[et al.]. Interactive Echocardiography Translation Using Few-Shot GAN Transfer Learning. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1139335
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Teng, Long& Fu, ZhongLiang& Ma, Qian& Yao, Yu& Zhang, Bing& Zhu, Kai…[et al.]. Interactive Echocardiography Translation Using Few-Shot GAN Transfer Learning. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1139335
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139335
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر