42 results on '"Cohan, Richard H."'
Search Results
2. Effect of computerized decision support on diagnostic accuracy and intra-observer variability in multi-institutional observer performance study for bladder cancer treatment response assessment in CT urography
3. Deep Learning Based Bladder Cancer Treatment Response Assessment.
4. 2D and 3D Bladder Segmentation using U-Net-based Deep-Learning.
5. Bladder Cancer Staging in CT Urography: Estimation and Validation of Decision Thresholds for a Radiomics-Based Decision Support System.
6. Multi-institutional observer performance study for bladder cancer treatment response assessment in CT urography with and without computerized decision support
7. Bladder wall segmentation using U-net based deep learning
8. Convolutional neural network-based decision support system for bladder cancer staging in CT urography: decision threshold estimation and validation
9. Computer-aided Detection of Bladder Wall Thickening in CT Urography (CTU).
10. Bladder Cancer Staging in CT Urography: Effect of Stage Labels on Statistical Modeling of a Decision Support System.
11. Bladder Cancer Treatment Response Assessment with Radiomic, Clinical and Radiologist Semantic Features.
12. Bladder Cancer Treatment Response Assessment in CT Urography using Two-Channel Deep-Learning Network.
13. Deep learning based bladder cancer treatment response assessment
14. 2D and 3D bladder segmentation using U-Net-based deep-learning
15. Bladder cancer staging in CT urography: estimation and validation of decision thresholds for a radiomics-based decision support system
16. Survival prediction for patients with metastatic urothelial cancer after immunotherapy using machine learning.
17. Bladder cancer treatment response assessment in CT urography by using deep-learning and radiomics.
18. Bladder cancer segmentation using U-Net-based deep-learning.
19. Bladder cancer staging in CT urography: effect of stage labels on statistical modeling of a decision support system
20. Bladder cancer treatment response assessment with radiomic, clinical, and radiologist semantic features
21. Bladder cancer treatment response assessment in CT urography using two-channel deep-learning network
22. Bladder cancer treatment response assessment in CT urography by using deep-learning and radiomics
23. Bladder cancer segmentation using U-Net-based deep-learning
24. Automated segmentation of urinary bladder and detection of bladder lesions in multi-detector row CT urography.
25. Automated detection of ureteral wall thickening on multi-detector row CT urography.
26. Automated detection of ureter abnormalities on multi-detector row CT urography.
27. Computer-aided detection of bladder mass within non-contrast-enhanced region of CT Urography (CTU)
28. Automatic detection of ureter lesions in CT urography
29. Automatic staging of bladder cancer on CT urography
30. Effect of computerized decision support on diagnostic accuracy and intra-observer variability in multi-institutional observer performance study for bladder cancer treatment response assessment in CT urography.
31. Ureter segmentation in CT urography (CTU) by COMPASS with multiscale Hessian enhancement
32. Computer-aided detection of bladder mass within contrast-enhanced region of CTU
33. COMPASS-based ureter segmentation in CT urography (CTU)
34. Segmentation of urinary bladder in CT urography (CTU) using CLASS with enhanced contour conjoint procedure
35. Comparison of CLASS and ITK-SNAP in segmentation of urinary bladder in CT urography
36. Multi-institutional observer performance study for bladder cancer treatment response assessment in CT urography with and without computerized decision support.
37. Segmentation of urinary bladder in CT Urography (CTU) using CLASS
38. Computer-aided detection of bladder wall thickening in CT urography (CTU)
39. Bladder cancer treatment response assessment using deep learning in CT with transfer learning
40. Segmentation of inner and outer bladder wall using deep-learning convolutional neural network in CT urography
41. Computer-aided detection of bladder masses in CT urography (CTU)
42. Comparison of bladder segmentation using deep-learning convolutional neural network with and without level sets
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