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Table of contents
1 Introduction
1.1 Thesis Context
1.2 Thesis Motivation and Objectives
1.3 Thesis Contributions
1.4 Thesis Overview
2 Brain Tumor Segmentation
2.1 Introduction
2.2 Survey on MRI image segmentation
2.2.1 Classical approaches
2.2.2 Modern approaches
2.2.3 Discussion
2.3 Glioblastoma brain tumors
2.4 BRATS datasets
2.5 MRI image quality limitations
2.6 Evaluation metrics
2.7 Discussion and Conclusion
3 Background Theory: Convolutional Neural Networks
3.1 Introduction
3.2 Convolutional Neural Networks
3.2.1 CNNs operations
3.2.2 Forward Propagation
3.2.3 Backward propagation
3.2.4 Regularization Dropout
3.3 Discussion and Conclusion
4 Brain Tumor Segmentation with Deep Neural Networks
4.1 Introduction
4.2 End-to-End incremental Deep learning
4.2.1 Problem statement
4.2.2 Incremental XCNet Algorithm
4.2.3 ELOBA Algorithm
4.2.4 Experiments and Results
4.2.5 Discussion and conclusion
4.3 Deep Learning-based selective attention
4.3.1 Problem statement
4.3.2 Visual areas-based interconnected modules
4.3.3 Overlapping Patches
4.3.4 Class-Weighting technique
4.3.5 Experiments and Results
4.3.6 Discussion and Conclusion
5 Deep learning with unbalanced data and misclassified regions
5.1 Introduction
5.2 Deep learning with Online class-weighting
5.2.1 Problem statement
5.2.2 Online class-weighting approach
5.2.3 Experiments and Results
5.2.4 Discussion and Conclusion
5.3 Boosting performance using deep transfer learning approach
5.3.1 Problem statement
5.3.2 Methods
5.3.3 Experiments and Results
5.3.4 Discussion and Conclusion
6 Conclusion and perspectives
6.1 Summary of contributions
6.2 Perspectives
6.3 List of publications
