Computer-Aided Diagnosis of Congenital Abnormalities of the Kidney and Urinary Tract in Children Using a Multi-Instance Deep Learning Method Based on Ultrasound Imaging Data.
Computer-Aided Diagnosis of Congenital Abnormalities of the Kidney and Urinary Tract in Children Using a Multi-Instance Deep Learning Method Based on Ultrasound Imaging Data.
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DOI：
10.1109/isbi45749.2020.9098506
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发表时间：
2020-04
期刊：
影响因子：
--
通讯作者：
Fan Y
中科院分区：
文献类型：
--
作者：
Yin S;Peng Q;Li H;Zhang Z;You X;Fischer K;Furth SL;Tasian GE;Fan Y
超声图像被广泛用于诊断先天性肾和尿路异常（CAKUT）。由于典型的临床超声图像捕获肾脏特定视图平面的二维信息，而同一肾脏在不同平面上的图像具有不同的外观，因此开发一种对不同视图超声图像具有鲁棒性的计算机辅助诊断工具具有挑战性。为了克服这一问题，我们开发了一种基于临床超声图像区分CAKUT儿童与对照组的多实例深度学习方法，旨在基于超声图像数据自动诊断儿童CAKUT。特别是，我们开发了一种多实例深度学习方法，以建立鲁棒模式分类器，根据常规临床护理中获得的矢状和横向超声图像来区分CAKUT儿童和对照组。该分类器基于从预训练的深度学习模型中迁移学习得出的图像特征，并使用平均池化算子融合实例级分类结果。实验结果表明，多实例深度学习分类器比建立在单个矢状切片或单个横向切片上的分类器表现更好。
Ultrasound images are widely used for diagnosis of congenital abnormalities of the kidney and urinary tract (CAKUT). Since a typical clinical ultrasound image captures 2D information of a specific view plan of the kidney and images of the same kidney on different planes have varied appearances, it is challenging to develop a computer aided diagnosis tool robust to ultrasound images in different views. To overcome this problem, we develop a multi-instance deep learning method for distinguishing children with CAKUT from controls based on their clinical ultrasound images, aiming to automatic diagnose the CAKUT in children based on ultrasound imaging data. Particularly, a multi-instance deep learning method was developed to build a robust pattern classifier to distinguish children with CAKUT from controls based on their ultrasound images in sagittal and transverse views obtained during routine clinical care. The classifier was built on imaging features derived using transfer learning from a pre-trained deep learning model with a mean pooling operator for fusing instance-level classification results. Experimental results have demonstrated that the multi-instance deep learning classifier performed better than classifiers built on either individual sagittal slices or individual transverse slices.
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影响因子：
8
作者：
Carbonneau, Marc-Andre;Cheplygina, Veronika;Gagnon, Ghyslain
通讯作者：
Gagnon, Ghyslain
DOI：
10.1055/s-0032-1325611
发表时间：
2012-12-01
期刊：
Ultraschall in der Medizin (Stuttgart, Germany : 1980)
影响因子：
--
作者：
Richter-Rodier, M;Lange, A E;Haas, J P
通讯作者：
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影响因子：
2
作者：
Zheng, Q.;Furth, S. L.;Fan, Y.
通讯作者：
Fan, Y.
重新审视多实例神经网络
影响因子：
8
作者：
Wang, Xinggang;Yan, Yongluan;Liu, Wenyu
通讯作者：
Liu, Wenyu
DOI：
10.1016/j.juro.2011.05.059
发表时间：
2011-10
期刊：
The Journal of urology
影响因子：
--
作者：
Dodson JL;Jerry-Fluker JV;Ng DK;Moxey-Mims M;Schwartz GJ;Dharnidharka VR;Warady BA;Furth SL
通讯作者：
Furth SL