Search Results for author: Ahmad Chaddad

Found 7 papers, 0 papers with code

Deep radiomic signature with immune cell markers predicts the survival of glioma patients

no code implementations9 Jun 2022 Ahmad Chaddad, Paul Daniel Mingli Zhang, Saima Rathore, Paul Sargos, Christian Desrosiers, Tamim Niazi

These results demonstrate the usefulness of proposed DRFs as non-invasive biomarker for predicting treatment response in patients with brain tumors.

Modeling of Textures to Predict Immune Cell Status and Survival of Brain Tumour Patients

no code implementations4 Jun 2022 Ahmad Chaddad, Mingli Zhang, Lama Hassan, Tamim Niazi

Combined the immune markers with DRFs and clinical variables, Kaplan-Meier estimator and Log-rank test achieved the most significant difference between predicted groups of patients (short-term versus long-term survival) with p\,=\, 4. 31$\times$10$^{-7}$ compared to p\,=\, 0. 03 for Immune cell markers, p\,=\, 0. 07 for clinical variables , and p\,=\, 1. 45$\times$10$^{-5}$ for DRFs.

Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-ray Images

no code implementations4 Jun 2022 Ahmad Chaddad, Lama Hassan, Christian Desrosiers

Our results suggest that the proposed GMM-CNN features could improve the prediction of COVID-19 in chest computed tomography and X-ray scans.

Future Artificial Intelligence tools and perspectives in medicine

no code implementations4 Jun 2022 Ahmad Chaddad, Yousef Katib, Lama Hassan

Purpose of review: Artificial intelligence (AI) has become popular in medical applications, specifically as a clinical support tool for computer-aided diagnosis.

Deep radiomic features from MRI scans predict survival outcome of recurrent glioblastoma

no code implementations15 Nov 2019 Ahmad Chaddad, Saima Rathore, Mingli Zhang, Christian Desrosiers, Tamim Niazi

This paper proposes to use deep radiomic features (DRFs) from a convolutional neural network (CNN) to model fine-grained texture signatures in the radiomic analysis of recurrent glioblastoma (rGBM).

General Classification

Modeling Information Flow Through Deep Neural Networks

no code implementations29 Nov 2017 Ahmad Chaddad, Behnaz Naisiri, Marco Pedersoli, Eric Granger, Christian Desrosiers, Matthew Toews

This paper proposes a principled information theoretic analysis of classification for deep neural network structures, e. g. convolutional neural networks (CNN).

Classification General Classification +1

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