Computer-Aided Diagnosis Systems for Acute Renal Transplant Rejection: Challenges and Methodologies

2013 ◽  
pp. 1-35 ◽  
Author(s):  
Mahmoud Mostapha ◽  
Fahmi Khalifa ◽  
Amir Alansary ◽  
Ahmed Soliman ◽  
Jasjit Suri ◽  
...  
Keyword(s):  
Renal Transplant ◽  
Transplant Rejection ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Renal Transplant Rejection

Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI

2019 ◽  
Vol 66 (2) ◽  
pp. 539-552 ◽  
Author(s):  
Mohamed Shehata ◽  
Fahmi Khalifa ◽  
Ahmed Soliman ◽  
Mohammed Ghazal ◽  
Fatma Taher ◽  
...  
Keyword(s):  
Renal Transplant ◽  
Early Detection ◽  
Transplant Rejection ◽  
Diagnostic System ◽  
Diffusion Weighted Mri ◽  
Diffusion Weighted ◽  
Computer Aided ◽  
Renal Transplant Rejection

Influence of mycophenolate mofetil on BTLA and ICOS signaling in renal transplant rejection

2010 ◽  
Vol 34 (8) ◽  
pp. S70-S70
Author(s):  
Yan Wang ◽  
Chuan Tian ◽  
Chun Mei Wang ◽  
Chun Guang Fan ◽  
Gang Liu
Keyword(s):  
Mycophenolate Mofetil ◽  
Renal Transplant ◽  
Transplant Rejection ◽  
Renal Transplant Rejection

Pattern-Recognition: A Comparison of the Performance of Clinicians and Non-Clinicians - with a Note on the Performance of a Computer-Based System

1972 ◽  
Vol 11 (01) ◽  
pp. 32-37 ◽  
Author(s):  
F. T. DE DOMBAL ◽  
J. C. HORROCKS ◽  
J. R. STANILAND ◽  
P. J. GUILLOU
Keyword(s):  
Pattern Recognition ◽  
Computer System ◽  
Computer Aided Diagnosis ◽  
Clinical Interview ◽  
Computer Performance ◽  
Pattern Size ◽  
Computer Aided ◽  
Computer Based ◽  
Aided Diagnosis ◽  
Better Than

This paper describes a series of 10,500 attempts at »pattern-recognition« by two groups of humans and a computer based system. There was little difference between the performances of 11 clinicians and 11 other persons of comparable intellectual capability. Both groups’ performances were related to the pattern-size, the accuracy diminishing rapidly as the patterns grew larger. By contrast the computer system increased its accuracy as the patterns increased in size.It is suggested (a) that clinicians are very little better than others at pattem-recognition, (b) that the clinician is incapable of analysing on a probabilistic basis the data he collects during a traditional clinical interview and examination and (c) that the study emphasises once again a major difference between human and computer performance. The implications as - regards human- and computer-aided diagnosis are discussed.

COMPUTER-AIDED DIAGNOSIS OF GASTRIC LESIONS USING MAGNIFYING NARROW BAND IMAGING ENDOSCOPY

2019 ◽  
Author(s):  
S Kashin ◽  
R Kuvaev ◽  
E Kraynova ◽  
H Edelsbrunner ◽  
O Dunaeva ◽  
...  
Keyword(s):  
Narrow Band ◽  
Narrow Band Imaging ◽  
Computer Aided Diagnosis ◽  
Gastric Lesions ◽  
Computer Aided ◽  
Aided Diagnosis
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