Supervised regularized canonical correlation analysis: integrating histologic and proteomic data for predicting biochemical failures. Read more about Supervised regularized canonical correlation analysis: integrating histologic and proteomic data for predicting biochemical failures.
Evaluating feature selection strategies for high dimensional, small sample size datasets. Read more about Evaluating feature selection strategies for high dimensional, small sample size datasets.
MULTI-MODAL DATA FUSION SCHEMES FOR INTEGRATED CLASSIFICATION OF IMAGING AND NON-IMAGING BIOMEDICAL DATA. Read more about MULTI-MODAL DATA FUSION SCHEMES FOR INTEGRATED CLASSIFICATION OF IMAGING AND NON-IMAGING BIOMEDICAL DATA.
Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data. Read more about Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data.
Supervised regularized canonical correlation analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgery. Read more about Supervised regularized canonical correlation analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgery.
Variable importance in nonlinear kernels (VINK): classification of digitized histopathology. Read more about Variable importance in nonlinear kernels (VINK): classification of digitized histopathology.
Cell orientation entropy (COrE): predicting biochemical recurrence from prostate cancer tissue microarrays. Read more about Cell orientation entropy (COrE): predicting biochemical recurrence from prostate cancer tissue microarrays.
Co-occurring gland angularity in localized subgraphs: predicting biochemical recurrence in intermediate-risk prostate cancer patients. Read more about Co-occurring gland angularity in localized subgraphs: predicting biochemical recurrence in intermediate-risk prostate cancer patients.
Supervised multi-view canonical correlation analysis (sMVCCA): integrating histologic and proteomic features for predicting recurrent prostate cancer. Read more about Supervised multi-view canonical correlation analysis (sMVCCA): integrating histologic and proteomic features for predicting recurrent prostate cancer.
Prostate Cancer Recurrence Can Be Predicted By Measuring Cell Graph and Nuclear Shape Parameters in the Benign Cancer-Adjacent Field of Surgical Specimens Read more about Prostate Cancer Recurrence Can Be Predicted By Measuring Cell Graph and Nuclear Shape Parameters in the Benign Cancer-Adjacent Field of Surgical Specimens
