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
Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Hangfan Liu, Katherine Fischer, Susan L Furth, Gregory E Tasian, Yong Fan
{"title":"Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging.","authors":"Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Hangfan Liu, Katherine Fischer, Susan L Furth, Gregory E Tasian, Yong Fan","doi":"10.1007/978-3-030-32689-0_15","DOIUrl":"10.1007/978-3-030-32689-0_15","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":92968,"journal":{"name":"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...","volume":"11840 ","pages":"146-154"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6938161/pdf/nihms-1060591.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37504845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"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","authors":"","doi":"10.1007/978-3-030-32689-0","DOIUrl":"https://doi.org/10.1007/978-3-030-32689-0","url":null,"abstract":"","PeriodicalId":92968,"journal":{"name":"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...","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90716427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}