Radiomics-based classification of autosomal dominant polycystic kidney disease (ADPKD) Mayo imaging classification (MIC) and the effect of gray-level discretization

Author:

Kremer Linnea E.,Fosso Boris,Groothuis Lucy,Chapman Arlene,Armato Samuel G.

Publisher

SPIE

Reference11 articles.

1. Estimation of Changes in Kidney Volume Growth Rate in ADPKD

2. The value of genotypic and imaging information to predict functional and structural outcomes in ADPKD

3. CRISP Investigators;Kline;Image texture features predict renal function decline in patients with autosomal dominant polycystic kidney disease. Kidney Int,2017

4. A MRI-based radiomics nomogram for evaluation of renal function in ADPKD

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1. Magnetic resonance imaging preprocessing and radiomic features for classification of autosomal dominant polycystic kidney disease genotype;Journal of Medical Imaging;2023-12-27

2. Detection of Kidney Disease in X-Ray Images Using Machine Learning on MobileNetV3 CNN Model;2023 2nd International Conference on Futuristic Technologies (INCOFT);2023-11-24

3. Kidney Disease X-Ray Image Classification Using a ResNet50V2 Model based Machine Learning Approach;2023 2nd International Conference on Futuristic Technologies (INCOFT);2023-11-24

4. Kidney Disease Classification Using Machine Learning Approach on DenseNet201 Model using Xray Images;2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI);2023-10-19

5. Abdominal Imaging in ADPKD: Beyond Total Kidney Volume;Journal of Clinical Medicine;2023-08-05

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