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Full-Text Articles in Engineering
Deep Learning With Context Encoding For Semantic Brain Tumor Segmentation And Patient Survival Prediction, Linmin Pei, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin
Deep Learning With Context Encoding For Semantic Brain Tumor Segmentation And Patient Survival Prediction, Linmin Pei, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
One of the most challenging problems encountered in deep learning-based brain tumor segmentation models is the misclassification of tumor tissue classes due to the inherent imbalance in the class representation. Consequently, strong regularization methods are typically considered when training large-scale deep learning models for brain tumor segmentation to overcome undue bias towards representative tissue types. However, these regularization methods tend to be computationally exhaustive, and may not guarantee the learning of features representing all tumor tissue types that exist in the input MRI examples. Recent work in context encoding with deep CNN models have shown promise for semantic segmentation of …
Feature-Guided Deep Radiomics For Glioblastoma Patient Survival Prediction, Zeina A. Shboul, Mahbubul Alam, Lasitha Vidyaratne, Linmin Pei, Mohamed I. Elbakary, Khan M. Iftekharuddin
Feature-Guided Deep Radiomics For Glioblastoma Patient Survival Prediction, Zeina A. Shboul, Mahbubul Alam, Lasitha Vidyaratne, Linmin Pei, Mohamed I. Elbakary, Khan M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
Glioblastoma is recognized as World Health Organization (WHO) grade IV glioma with an aggressive growth pattern. The current clinical practice in diagnosis and prognosis of Glioblastoma using MRI involves multiple steps including manual tumor sizing. Accurate identification and segmentation of multiple abnormal tissues within tumor volume in MRI is essential for precise survival prediction. Manual tumor and abnormal tissue detection and sizing are tedious, and subject to inter-observer variability. Consequently, this work proposes a fully automated MRI-based glioblastoma and abnormal tissue segmentation, and survival prediction framework. The framework includes radiomics feature-guided deep neural network methods for tumor tissue segmentation; followed …