Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures : first International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Sh...最新文献

英文中文

Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging. 基于图卷积神经网络的多实例深度学习超声诊断肾脏疾病。

Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures : first International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Sh...

Pub Date : 2019-10-01 Epub Date: 2019-10-07 DOI: 10.1007/978-3-030-32689-0_15

Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Hangfan Liu, Katherine Fischer, Susan L Furth, Gregory E Tasian, Yong Fan

Ultrasound imaging (US) is commonly used in nephrology for diagnostic studies of the kidneys and lower urinary tract. However, it remains challenging to automate the disease diagnosis based on clinical 2D US images since they provide partial anatomic information of the kidney and the 2D images of the same kidney may have heterogeneous appearance. To overcome this challenge, we develop a novel multi-instance deep learning method to build a robust classifier by treating multiple 2D US images of each individual subject as multiple instances of one bag. Particularly, we adopt convolutional neural networks (CNNs) to learn instance-level features from 2D US kidney images and graph convolutional networks (GCNs) to further optimize the instance-level features by exploring potential correlation among instances of the same bag. We also adopt a gated attention-based MIL pooling to learn bag-level features using full-connected neural networks (FCNs). Finally, we integrate both instance-level and bag-level supervision to further improve the bag-level classification accuracy. Ablation studies and comparison results have demonstrated that our method could accurately diagnose kidney diseases using ultrasound imaging, with better performance than alternative state-of-the-art multi-instance deep learning methods.

超声成像(US)通常用于肾脏和下尿路的肾脏病诊断研究。然而，基于临床二维图像的疾病自动诊断仍然具有挑战性，因为它们提供了肾脏的部分解剖信息，而同一肾脏的二维图像可能具有异质外观。为了克服这一挑战，我们开发了一种新的多实例深度学习方法，通过将每个单独主题的多个2D美国图像作为一个袋子的多个实例来构建鲁棒分类器。特别是，我们采用卷积神经网络(cnn)从2D美国肾脏图像中学习实例级特征，并通过探索同一袋子的实例之间的潜在相关性来进一步优化实例级特征。我们还采用了一种基于门控注意力的MIL池，使用全连接神经网络(fcn)来学习袋级特征。最后，我们将实例级监督和袋级监督相结合，进一步提高袋级分类的准确率。消融研究和对比结果表明，我们的方法可以使用超声成像准确诊断肾脏疾病，性能优于其他最先进的多实例深度学习方法。

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引用次数: 0

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures: First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings 机器学习在医学成像和基于临床图像的程序中安全使用的不确定性:第一届国际研讨会，2019年，和第八届国际研讨会，CLIP 2019，与MICCAI 2019一起举行，中国深圳，2019年10月17日，会议记录

Pub Date : 2019-01-01 DOI: 10.1007/978-3-030-32689-0

引用次数: 3

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全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

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