AGE-CONDITIONED SYNTHESIS OF PEDIATRIC COMPUTED TOMOGRAPHY WITH AUXILIARY CLASSIFIER GENERATIVE ADVERSARIAL NETWORKS.
AGE-CONDITIONED SYNTHESIS OF PEDIATRIC COMPUTED TOMOGRAPHY WITH AUXILIARY CLASSIFIER GENERATIVE ADVERSARIAL NETWORKS.
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DOI：
10.1109/isbi45749.2020.9098623
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发表时间：
2020-04
期刊：
影响因子：
--
通讯作者：
Ye DH
中科院分区：
文献类型：
--
作者：
Kan CNE;Maheenaboobacker N;Ye DH
深度学习是计算机断层扫描（CT）图像处理（如器官分割）中流行且强大的工具，但其对大型训练数据集的要求仍然是一个挑战。尽管儿童在成长过程中存在很大的解剖差异，但由于辐射对儿童的风险，儿童CT扫描的训练数据集尤其难以获得。在本文中，我们提出了一种基于年龄信息的辅助分类器生成对抗网络（ACGAN）结构有条件地合成真实儿童CT图像的方法。该网络生成年龄条件下的高分辨率CT图像，以丰富儿科训练数据集。
Deep learning is a popular and powerful tool in computed tomography (CT) image processing such as organ segmentation, but its requirement of large training datasets remains a challenge. Even though there is a large anatomical variability for children during their growth, the training datasets for pediatric CT scans are especially hard to obtain due to risks of radiation to children. In this paper, we propose a method to conditionally synthesize realistic pediatric CT images using a new auxiliary classifier generative adversarial network (ACGAN) architecture by taking age information into account. The proposed network generated age-conditioned high-resolution CT images to enrich pediatric training datasets.
影响因子：
168.9
作者：
Pearce, Mark S.;Salotti, Jane A.;Little, Mark P.;McHugh, Kieran;Lee, Choonsik;Kim, Kwang Pyo;Howe, Nicola L.;Ronckers, Cecile M.;Rajaraman, Preetha;Craft, Alan W.;Parker, Louise;de Gonzalez, Amy Berrington
通讯作者：
de Gonzalez, Amy Berrington
影响因子：
22.7
作者：
Goodfellow, Ian;Pouget-Abadie, Jean;Bengio, Yoshua
通讯作者：
Bengio, Yoshua