Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging.

TitleMulti-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging.
Publication TypeJournal Article
Year of Publication2019
AuthorsYin S, Peng Q, Li H, Zhang Z, You X, Liu H, Fischer K, Furth SL, Tasian GE, Fan Y
JournalUncertain Safe Util Machine Learn Med Imaging Clin Image Based Proced (2019)
Volume11840
Pagination146-154
Date Published2019 Oct
Abstract

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.

DOI10.1007/978-3-030-32689-0_15
Alternate JournalUncertain Safe Util Machine Learn Med Imaging Clin Image Based Proced (2019)
PubMed ID31893285
PubMed Central IDPMC6938161
Grant ListP50 DK114786 / DK / NIDDK NIH HHS / United States
R21 DK117297 / DK / NIDDK NIH HHS / United States
UL1 TR001878 / TR / NCATS NIH HHS / United States