DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation

Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These techniques have even outperformed human experts in controlled evaluation conditions such as Longitudinal MS Lesion Segmentation Challenge (ISBI Challenge). However state-of-the-art approaches trained to perform well on highly-controlled datasets fail to generalize on clinical data from unseen datasets. Instead of proposing another improvement of the segmentation accuracy, we propose a novel method robust to domain shift and performing well on unseen datasets, called DeepLesionBrain (DLB). This generalization property results from three main contributions. First, DLB is based on a large group of compact 3D CNNs. This spatially distributed strategy ensures a robust prediction despite the risk of generalization failure of some individual networks. Second, DLB includes a new image quality data augmentation to reduce dependency to training data specificity (e.g., acquisition protocol). Finally, to learn a more generalizable representation of MS lesions, we propose a hierarchical specialization learning (HSL). HSL is performed by pre-training a generic network over the whole brain, before using its weights as initialization to locally specialized networks. By this end, DLB learns both generic features extracted at global image level and specific features extracted at local image level. DLB generalization was validated in cross-dataset experiments on MSSEG'16, ISBI challenge, and in-house datasets. During experiments, DLB showed higher segmentation accuracy, better segmentation consistency and greater generalization performance compared to state-of-the-art methods. Therefore, DLB offers a robust framework well-suited for clinical practice

Multi-Source Data Fusion for Cyberattack Detection in Power Systems

Cyberattacks can cause a severe impact on power systems unless detected early. However, accurate and timely detection in critical infrastructure systems presents challenges, e.g., due to zero-day vulnerability exploitations and the cyber-physical nature of the system coupled with the need for high reliability and resilience of the physical system. Conventional rule-based and anomaly-based intrusion detection system (IDS) tools are insufficient for detecting zero-day cyber intrusions in the industrial control system (ICS) networks. Hence, in this work, we show that fusing information from multiple data sources can help identify cyber-induced incidents and reduce false positives. Specifically, we present how to recognize and address the barriers that can prevent the accurate use of multiple data sources for fusion-based detection. We perform multi-source data fusion for training IDS in a cyber-physical power system testbed where we collect cyber and physical side data from multiple sensors emulating real-world data sources that would be found in a utility and synthesizes these into features for algorithms to detect intrusions. Results are presented using the proposed data fusion application to infer False Data and Command injection-based Man-in- The-Middle (MiTM) attacks. Post collection, the data fusion application uses time-synchronized merge and extracts features followed by pre-processing such as imputation and encoding before training supervised, semi-supervised, and unsupervised learning models to evaluate the performance of the IDS. A major finding is the improvement of detection accuracy by fusion of features from cyber, security, and physical domains. Additionally, we observed the co-training technique performs at par with supervised learning methods when fed with our features

Scalable deep feature learning for person re-identification

Person Re-identification (Person Re-ID) is one of the fundamental and critical tasks of the video surveillance systems. Given a probe image of a person obtained from one Closed Circuit Television (CCTV) camera, the objective of Person Re-ID is to identify the same person from a large gallery set of images captured by other cameras within the surveillance system. By successfully associating all the pedestrians, we can quickly search, track and even plot a movement trajectory of any person of interest within a CCTV system. Currently, most search and re-identification jobs are still processed manually by police or security officers. It is desirable to automate this process in order to reduce an enormous amount of human labour and increase the pedestrian tracking and retrieval speed. However, Person Re-ID is a challenging problem because of so many uncontrolled properties of a multi-camera surveillance system: cluttered backgrounds, large illumination variations, different human poses and different camera viewing angles. The main goal of this thesis is to develop deep learning based person reidentification models for real-world deployment in surveillance system. This thesis focuses on learning and extracting robust feature representations of pedestrians. In this thesis, we first proposed two supervised deep neural network architectures. One end-to-end Siamese network is developed for real-time person matching tasks. It focuses on extracting the correspondence feature between two images. For an offline person retrieval application, we follow the commonly used feature extraction with distance metric two-stage pipline and propose a strong feature embedding extraction network. In addition, we surveyed many valuable training techniques proposed recently in the literature to integrate them with our newly proposed NP-Triplet xiii loss to construct a strong Person Re-ID feature extraction model. However, during the deployment of the online matching and offline retrieval system, we realise the poor scalability issue in most supervised models. A model trained from labelled images obtained from one system cannot perform well on other unseen systems. Aiming to make the Person Re-ID models more scalable for different surveillance systems, the third work of this thesis presents cross-Dataset feature transfer method (MMFA). MMFA can train and transfer the model learned from one system to another simultaneously. Our goal to create a more scalable and robust person reidentification system did not stop here. The last work of this thesis, we address the limitation of MMFA structure and proposed a multi-dataset feature generalisation approach (MMFA-AAE), which aims to learn a universal feature representation from multiple labelled datasets. Aiming to facilitate the research towards Person Re-ID applications in more realistic scenarios, a new datasets ROSE-IDENTITY-Outdoor (RE-ID-Outdoor) has been collected and annotated with the largest number of cameras and 40 mid-level attributes

Learning Transferable Features From Different Domains

Les progrès récents en matière d'apprentissage automatique supposent généralement que les données d'apprentissage et de test proviennent de la même distribution de données. Cependant, dans la pratique, les données peuvent être collectées séparément comme des ensembles de données différents. Apprendre à partir de données provenant de plusieurs domaines sources et les généraliser à un autre domaine est un problème crucial de l'apprentissage automatique. Nous abordons ce type de problème dans le contexte de l'apprentissage par transfert (TL), notamment l'adaptation de domaine (DA), la généralisation de domaine (DG) et l'apprentissage multi-tâches (MTL), et ce dans le but de transférer les caractéristiques invariantes communes à de nouveaux domaines. Nous avons étudié ce type d'apprentissage par transfert sous différents aspects, y compris les problèmes liés au décalage conditionnel dans l'adaptation de domaine, les problèmes de désalignement sémantique et de décalage d'étiquettes dans la généralisation de domaine et l'apprentissage multi-tâches en parvenant à plusieurs résultats. Concrètement, nous explorons d'abord les problèmes de décalage conditionnel (DA) avec une stratégie d'apprentissage actif pour interroger les instances les plus informatives dans le domaine cible afin de faire migrer le terme de désaccord entre les fonctions d'étiquetage des domaines source et cible. Nous explorons ensuite les similitudes de catégories dans les problèmes liés à la généralisation de domaine (DG) via l'entraînement adversarial basé sur le transport optimal avec un objectif d'apprentissage de similarité métrique afin d'améliorer la correspondance au niveau du domaine et de la classe pour les problèmes DG. Nous étudions ensuite, plus en détail les relations entre les étiquettes et la sémantique dans le MTL, où nous fournissons une compréhension théorique de la manière de contrôler les divergences entre les étiquettes et la distribution sémantique. Enfin, nous étendons l'analyse théorique sur la façon d'exploiter les étiquettes et l'information sémantique dans la généralisation de domaine (DG), en fournissant une première analyse pour comprendre les propriétés de généralisation dans le contrôle des divergences de distribution des étiquettes et de la sémantique. Pour chaque travail reflété dans cette thèse, nous menons des expériences approfondies afin de démontrer l'efficacité et les objectifs d'apprentissage. Les résultats expérimentaux confirment que nos méthodes parviennent aux performances souhaitées et indiquées par les principes d'analyse et d'apprentissage, ce qui valide les contributions de cette thèse.Recent machine learning progresses usually assume the data for training and testing are from the same data distribution. However, in practice, the data might be gathered separately as different datasets. To learn data from several source domains and generalize to another domain, is a crucial problem in machine learning. We tackle this kind of problem in the context of Transfer Learning (TL), including Domain Adaptation (DA), Domain Generalization (DG) and Multi-task Learning (MTL), with the sake of transferring the common invariant features to new domains. We have investigated this kind of transfer learning method in several different aspects, including the conditional shift problems in domain adaptation, semantic misalignment and label shift problems in domain generalization and multi-task learning problems with several accomplishments. Concretely, we first explore the conditional shift problems DA with an active learning strategy to query the most informative instances in the target domain to migrate the disagreement term between the source and target domain labelling functions. We then explore the category similarities in the DG problems via the optimal transport-based adversarial training with a metric similarity learning objective to enhance both the domain-level and class-level matching for DG problems. After that, we further investigate the label and semantic relations in MTL, where we provide the first theoretical understanding of how to control the label and semantic distribution divergences. Lastly, we extend the theoretical analysis on how to leverage the label and semantic information in DG, providing the first analysis to understand the generalization properties on controlling the label and semantic distribution divergences. For each work reflected in this thesis, we also conduct intensive experiments to demonstrate the effectiveness and learning objectives. The experimental results confirm that our methods achieve the desired performance indicated by the analysis and learning principles, which confirms the contributions of this thesis