Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.

The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Toward this, we use an existing brain parcellation atlas in the Montreal Neurological Institute (MNI) space and map this atlas to the individual subject data. This mapped atlas in the subject data space is integrated with structural Magnetic Resonance (MR) imaging data, and patch-based neural networks, including 3D U-Net and DeepMedic, are trained to classify the different brain lesions. Multiple state-of-the-art neural networks are trained and integrated with XGBoost fusion in the proposed two-level ensemble method. The first level reduces the uncertainty of the same type of models with different seed initializations, and the second level leverages the advantages of different types of neural network models. The proposed location information fusion method improves the segmentation performance of state-of-the-art networks including 3D U-Net and DeepMedic. Our proposed ensemble also achieves better segmentation performance compared to the state-of-the-art networks in BraTS 2017 and rivals state-of-the-art networks in BraTS 2018. Detailed results are provided on the public multimodal brain tumor segmentation (BraTS) benchmarks

Artificial immune systems based committee machine for classification application

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion

Learning from imbalanced data in face re-identification using ensembles of classifiers

Face re-identification is a video surveillance application where systems for video-to-video face recognition are designed using faces of individuals captured from video sequences, and seek to recognize them when they appear in archived or live videos captured over a network of video cameras. Video-based face recognition applications encounter challenges due to variations in capture conditions such as pose, illumination etc. Other challenges in this application are twofold; 1) the imbalanced data distributions between the face captures of the individuals to be re-identified and those of other individuals 2) varying degree of imbalance during operations w.r.t. the design data. Learning from imbalanced data is challenging in general due in part to the bias of performance in most two-class classification systems towards correct classification of the majority (negative, or non-target) class (face images/frames captured from the individuals in not to be re-identified) better than the minority (positive, or target) class (face images/frames captured from the individual to be re-identified) because most two-class classification systems are intended to be used under balanced data condition. Several techniques have been proposed in the literature to learn from imbalanced data that either use data-level techniques to rebalance data (by under-sampling the majority class, up-sampling the minority class, or both) for training classifiers or use algorithm-level methods to guide the learning process (with or without cost sensitive approaches) such that the bias of performance towards correct classification of the majority class is neutralized. Ensemble techniques such as Bagging and Boosting algorithms have been shown to efficiently utilize these methods to address imbalance. However, there are issues faced by these techniques in the literature: (1) some informative samples may be neglected by random under-sampling and adding synthetic positive samples through upsampling adds to training complexity, (2) cost factors must be pre-known or found, (3) classification systems are often optimized and compared using performance measurements (like accuracy) that are unsuitable for imbalance problem; (4) most learning algorithms are designed and tested on a fixed imbalance level of data, which may differ from operational scenarios; The objective of this thesis is to design specialized classifier ensembles to address the issue of imbalance in the face re-identification application and as sub-goals avoiding the abovementioned issues faced in the literature. In addition achieving an efficient classifier ensemble requires a learning algorithm to design and combine component classifiers that hold suitable diversity-accuracy trade off. To reach the objective of the thesis, four major contributions are made that are presented in three chapters summarized in the following. In Chapter 3, a new application-based sampling method is proposed to group samples for under-sampling in order to improve diversity-accuracy trade-off between classifiers of the ensemble. The proposed sampling method takes the advantage of the fact that in face re-identification applications, facial regions of a same person appearing in a camera field of view may be regrouped based on their trajectories found by face tracker. A partitional Bagging ensemble method is proposed that accounts for possible variations in imbalance level of the operational data by combining classifiers that are trained on different imbalance levels. In this method, all samples are used for training classifiers and information loss is therefore avoided. In Chapter 4, a new ensemble learning algorithm called Progressive Boosting (PBoost) is proposed that progressively inserts uncorrelated groups of samples into a Boosting procedure to avoid loosing information while generating a diverse pool of classifiers. From one iteration to the next, the PBoost algorithm accumulates these uncorrelated groups of samples into a set that grows gradually in size and imbalance. This algorithm is more sophisticated than the one proposed in Chapter 3 because instead of training the base classifiers on this set, the base classifiers are trained on balanced subsets sampled from this set and validated on the whole set. Therefore, the base classifiers are more accurate while the robustness to imbalance is not jeopardized. In addition, the sample selection is based on the weights that are assigned to samples which correspond to their importance. In addition, the computation complexity of PBoost is lower than Boosting ensemble techniques in the literature for learning from imbalanced data because not all of the base classifiers are validated on all negative samples. A new loss factor is also proposed to be used in PBoost to avoid biasing performance towards the negative class. Using this loss factor, the weight update of samples and classifier contribution in final predictions are set according to the ability of classifiers to recognize both classes. In comparing the performance of the classifier systems in Chapter 3 and 4, a need is faced for an evaluation space that compares classifiers in terms of a suitable performance metric over all of their decision thresholds, different imbalance levels of test data, and different preference between classes. The F-measure is often used to evaluate two-class classifiers on imbalanced data, and no global evaluation space was available in the literature for this measure. Therefore, in Chapter 5, a new global evaluation space for the F-measure is proposed that is analogous to the cost curves for expected cost. In this space, a classifier is represented as a curve that shows its performance over all of its decision thresholds and a range of possible imbalance levels for the desired preference of true positive rate to precision. These properties are missing in ROC and precision-recall spaces. This space also allows us to empirically improve the performance of specialized ensemble learning methods for imbalance under a given operating condition. Through a validation, the base classifiers are combined using a modified version of the iterative Boolean combination algorithm such that the selection criterion in this algorithm is replaced by F-measure instead of AUC, and the combination is carried out for each operating condition. The proposed approaches in this thesis were validated and compared using synthetic data and videos from the Faces In Action, and COX datasets that emulate face re-identification applications. Results show that the proposed techniques outperforms state of the art techniques over different levels of imbalance and overlap between classes

Multiple Classifier Fusion using k-Nearest Localized Templates

Abstract. This paper presents a method for combining classifiers that uses knearest localized templates. The localized templates are estimated from a training set using C-means clustering algorithm, and matched to the decision profile of a new incoming sample by a similarity measure. The sample is assigned to the class which is most frequently represented among the k most similar templates. The appropriate value of k is determined according to the characteristics of the given data set. Experimental results on real and artificial data sets show that the proposed method performs better than the conventional fusion methods

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

A model not a prophet:Operationalising patient-level prediction using observational data networks

Improving prediction model developement and evaluation processes using observational health data

A model not a prophet:Operationalising patient-level prediction using observational data networks

Improving prediction model developement and evaluation processes using observational health data

Cooperative Training in Multiple Classifier Systems

Multiple classifier system has shown to be an effective technique for classification. The success of multiple classifiers does not entirely depend on the base classifiers and/or the aggregation technique. Other parameters, such as training data, feature attributes, and correlation among the base classifiers may also contribute to the success of multiple classifiers. In addition, interaction of these parameters with each other may have an impact on multiple classifiers performance. In the present study, we intended to examine some of these interactions and investigate further the effects of these interactions on the performance of classifier ensembles. The proposed research introduces a different direction in the field of multiple classifiers systems. We attempt to understand and compare ensemble methods from the cooperation perspective. In this thesis, we narrowed down our focus on cooperation at training level. We first developed measures to estimate the degree and type of cooperation among training data partitions. These evaluation measures enabled us to evaluate the diversity and correlation among a set of disjoint and overlapped partitions. With the aid of properly selected measures and training information, we proposed two new data partitioning approaches: Cluster, De-cluster, and Selection (CDS) and Cooperative Cluster, De-cluster, and Selection (CO-CDS). In the end, a comprehensive comparative study was conducted where we compared our proposed training approaches with several other approaches in terms of robustness of their usage, resultant classification accuracy and classification stability. Experimental assessment of CDS and CO-CDS training approaches validates their robustness as compared to other training approaches. In addition, this study suggests that: 1) cooperation is generally beneficial and 2) classifier ensembles that cooperate through sharing information have higher generalization ability compared to the ones that do not share training information