ArticleUncertainty 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...2019
Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging.
Article in 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..., 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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Who cites it
17 citing papers in PubMed, 40 citations in OpenAlex.
- Geometric Deep Learning for Multimodal Data in CKD.Journal of the American Society of Nephrology : JASN · 2025Article
- Enhancing Whole Slide Image Classification with Discriminative and Contrastive Learning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Artificial intelligence-aided ultrasound in renal diseases: a systematic review.Quantitative imaging in medicine and surgery · 2023Review
- ADCoC: Adaptive Distribution Modeling Based Collaborative Clustering for Disentangling Disease Heterogeneity from Neuroimaging Data.IEEE transactions on emerging topics in computational intelligence · 2023Article
- Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI.Neuroimage. Reports · 2023Article
- A narrative review on the application of artificial intelligence in renal ultrasound.Frontiers in oncology · 2023Review
- A two-stage renal disease classification based on transfer learning with hyperparameters optimization.Frontiers in medicine · 2023Article
- Deep learning and radiomics framework for PSMA-RADS classification of prostate cancer on PSMA PET.EJNMMI research · 2022Article
- Radiomics and machine learning for the diagnosis of pediatric cervical non-tuberculous mycobacterial lymphadenitis.Scientific reports · 2022Article
- Personalized application of machine learning algorithms to identify pediatric patients at risk for recurrent ureteropelvic junction obstruction after dismembered pyeloplasty.World journal of urology · 2022Article
- A Novel Hybrid Convolutional Neural Network Approach for the Stomach Intestinal Early Detection Cancer Subtype Classification.Computational intelligence and neuroscience · 2022Article
- Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future.Sensors (Basel, Switzerland) · 2021Review
- Intelligent Diagnosis of Thyroid Ultrasound Imaging Using an Ensemble of Deep Learning Methods.Medicina (Kaunas, Lithuania) · 2021Article
- Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency.Ultrasonography (Seoul, Korea) · 2021Article
- Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis.IEEE transactions on bio-medical engineering · 2020Article
- 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.Proceedings. IEEE International Symposium on Biomedical Imaging · 2020Article
- Using Deep Learning Algorithms to Grade Hydronephrosis Severity: Toward a Clinical Adjunct.Frontiers in pediatrics · 2020Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.