Characterising borderline areas in bladder tumour grading with Bayesian graph neural networks-Reference-Cited by-同舟云学术

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Characterising borderline areas in bladder tumour grading with Bayesian graph neural networks

Published:2024-05-27 Issue: Volume: Page:1-5
ISSN:
Container-title:2024 IEEE International Symposium on Biomedical Imaging (ISBI)
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Author:

Gao Shangqi¹,Browning Lisa²,Alham Nasullah Khalid³,Protheroe Andrew⁴,Edwards Keir¹,Hamblin Josh¹,Rittscher Jens³,Verrill Clare¹

Affiliation:

1. University of Oxford,Nuffield Department of Surgical Sciences,Oxford,UK

2. Oxford University Hospitals NHS Foundation Trust,Department of Cellular Pathology,Oxford,UK

3. University of Oxford,Department of Engineering Science Oxford,Oxford,UK

4. Oxford University Hospitals NHS Foundation Trust,Department of Oncology,Oxford,UK

Funder

Medical Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635485.pdf?arnumber=10635485

Reference13 articles.

1. Automated Detection and Grading of Non–Muscle-Invasive Urothelial Cell Carcinoma of the Bladder

2. Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

3. Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging

4. Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors

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