Case Discrimination: Self-supervised Feature Learning for the Classification of Focal Liver Lesions

Author(s):  
Haohua Dong ◽  
Yutaro Iwamoto ◽  
Xianhua Han ◽  
Lanfen Lin ◽  
Hongjie Hu ◽  
...  
2020 ◽  
Vol 130 ◽  
pp. 207-215 ◽  
Author(s):  
Jian Wang ◽  
Jing Li ◽  
Xian-Hua Han ◽  
Lanfen Lin ◽  
Hongjie Hu ◽  
...  

2016 ◽  
Vol 39 (2) ◽  
pp. 96-107 ◽  
Author(s):  
Deepti Mittal

This work is presented with the objective to assess quantitatively the impact of modified anisotropic diffusion–based enhancement method of Mittal et al. in computer-aided classification of focal liver lesions. This assessment was made before and after enhancement of clinically acquired ultrasound images with the comparison of (a) discrimination capability of radiologically important texture contrast feature using box plot and p-value statistics and (b) test results of designed computer-aided classification schemes to detect/classify focal liver tissues using receiver operating characteristic curves. The results reveal that the application of enhancement method on clinically acquired ultrasound image may effectively improve the confidence of clinicians/radiologists in computer-aided diagnostic solutions to detect and classify focal liver lesions.


2010 ◽  
Vol 32 (2) ◽  
pp. 352-359 ◽  
Author(s):  
Marius E. Mayerhoefer ◽  
Wolfgang Schima ◽  
Siegfried Trattnig ◽  
Katja Pinker ◽  
Vanessa Berger-Kulemann ◽  
...  

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