| Application of artificial neural network and multiple linear regression models for predicting survival time of patients with non-small cell cancer using multiple prognostic factors including FDG-PET measurements | 0 | 0.34 | 2014 |
| Reference-tissue correction of T2-weighted signal intensity for prostate cancer detection | 0 | 0.34 | 2014 |
| A study of T2-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer | 5 | 0.48 | 2013 |
| Computerized image analysis of cell-cell interactions in human renal tissue by using multi-channel immunoflourescent confocal microscopy | 0 | 0.34 | 2012 |
| Segmentation of prostatic glands in histology images | 7 | 0.88 | 2011 |
| A study of the effect of noise injection on the training of artificial neural networks | 6 | 0.56 | 2009 |
| A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. | 52 | 2.95 | 2005 |
| A study of several CAD methods for classification of clustered microcalcifications | 0 | 0.34 | 2005 |
| Uncertainty in the output of artificial neural networks. | 6 | 0.59 | 2003 |
| Automated selection of BI-RADS lesion descriptors for reporting calcifications in mammograms | 0 | 0.34 | 2003 |
| Improving the automated classification of clustered calcifications on mammograms through the improved detection of individual calcifications | 0 | 0.34 | 2002 |
| Eliminating false-positive microcalcification clusters in a mammography CAD scheme using a Bayesian neural network | 1 | 0.43 | 2001 |
| Requirement of microcalcification detection for computerized classification of malignant and benign clustered microcalcifications | 3 | 0.63 | 1998 |
| Benefits Of Computer-Aided Diagnosis (Cad) In Mammographic Diagnosis Of Malignant And Benign Clustered Microcalcifications | 0 | 0.34 | 1998 |
