A
Ali Islam
Researcher at University of Western Ontario
Publications - 50
Citations - 1002
Ali Islam is an academic researcher from University of Western Ontario. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 16, co-authored 46 publications receiving 877 citations. Previous affiliations of Ali Islam include General Electric & Robarts Research Institute.
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Journal ArticleDOI
Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation
TL;DR: A general, fully learning-based framework for direct bi-ventricular volume estimation, which removes user inputs and unreliable assumptions, and largely outperforms existing direct methods on a larger dataset of 100 subjects including both healthy and diseased cases with twice the number of subjects used in previous methods.
Journal ArticleDOI
Regional Assessment of Cardiac Left Ventricular Myocardial Function via MRI Statistical Features
Mariam Afshin,Ismail Ben Ayed,Kumaradevan Punithakumar,Max W. K. Law,Ali Islam,Aashish Goela,Terry M. Peters,Shuo Li +7 more
TL;DR: A real-time machine-learning approach which uses some image features that can be easily computed, but that nevertheless correlate well with the segmental cardiac function, and demonstrates that, over a cardiac cycle, the statistical features are related to the proportion of blood within each segment.
Book ChapterDOI
Direct estimation of cardiac bi-ventricular volumes with regression forests.
TL;DR: With the proposed method, the most daily-used estimation of cardiac function, e.g., ejection fraction, can be conducted in a much more efficient, accurate and convenient way.
Journal ArticleDOI
Direct Estimation of Cardiac Biventricular Volumes With an Adapted Bayesian Formulation
TL;DR: The proposed method introduces a novel likelihood function to exploit multiple appearance features, and a novel prior probability model to incorporate the area correlation between LV and RV cavities and enables a direct, efficient, and accurate assessment of global cardiac functions.
Journal ArticleDOI
Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning
TL;DR: In this paper, an integrated deep neural network Indices-Net is designed to jointly learn the representation and regression models for multitype cardiac indices estimation, which can improve the expressiveness of image representation with respect to cardiac indices.