Non-Invasive Fuhrman Grading of Clear Cell Renal Cell Carcinoma Using Computed Tomography Radiomics Features and Machine Learning

no code implementations • 26 Sep 2019 • Mostafa Nazari, Isaac Shiri, Ghasem Hajianfar, Niki Oveisi, Hamid Abdollahi, Mohammad Reza Deevband, Mehrdad Oveisi

The AUC was 0. 78 in logistic regression, 0. 62 in random forest, and 0. 83 in SVM model, respectively.

Computed Tomography (CT) Medical Physics Image and Video Processing Tissues and Organs

Non-Invasive MGMT Status Prediction in GBM Cancer Using Magnetic Resonance Images (MRI) Radiomics Features: Univariate and Multivariate Machine Learning Radiogenomics Analysis

no code implementations • 8 Jul 2019 • Ghasem Hajianfar, Isaac Shiri, Hassan Maleki, Niki Oveisi, Abbass Haghparast, Hamid Abdollahi, Mehrdad Oveisi

Conclusion: This study showed that radiomics using machine learning algorithms is a feasible, noninvasive approach to predict MGMT methylation status in GBM cancer patients Keywords: Radiomics, Radiogenomics, GBM, MRI, MGMT

BIG-bench Machine Learning feature selection

A Novel Deep Learning Based Approach for Left Ventricle Segmentation in Echocardiography: MFP-Unet

no code implementations • 25 Jun 2019 • Shakiba Moradi, Mostafa Ghelich-Oghli, Azin Alizadehasl, Isaac Shiri, Niki Oveisi, Mehrdad Oveisi, Majid Maleki, Jan Dhooge

Feature maps in all levels of the decoder path of U-net are concatenated, their depths are equalized, and up-sampled to a fixed dimension.

Decoder Image Segmentation +5