Automatic inference of cross-modal connection topologies for X-CNNs

This paper introduces a way to learn cross-modal convolutional neural network (X-CNN) architectures from a base convolutional network (CNN) and the training data to reduce the design cost and enable applying cross-modal networks in sparse data environments. Two approaches for building X-CNNs are presented. The base approach learns the topology in a data-driven manner, by using measurements performed on the base CNN and supplied data. The iterative approach performs further optimisation of the topology through a combined learning procedure, simultaneously learning the topology and training the network. The approaches were evaluated agains examples of hand-designed X-CNNs and their base variants, showing superior performance and, in some cases, gaining an additional 9% of accuracy. From further considerations, we conclude that the presented methodology takes less time than any manual approach would, whilst also significantly reducing the design complexity. The application of the methods is fully automated and implemented in Xsertion library

Modularity in artificial neural networks

Artificial neural networks are deep machine learning models that excel at complex artificial intelligence tasks by abstracting concepts through multiple layers of feature extraction. Modular neural networks are artificial neural networks that are composed of multiple subnetworks called modules. The study of modularity has a long history in the field of artificial neural networks and many of the actively studied models in the domain of artificial neural networks have modular aspects. In this work, we aim to formalize the study of modularity in artificial neural networks and outline how modularity can be used to enhance some neural network performance measures. We do an extensive review of the current practices of modularity in the literature. Based on that, we build a framework that captures the essential properties characterizing the modularization process. Using this modularization framework as an anchor, we investigate the use of modularity to solve three different problems in artificial neural networks: balancing latency and accuracy, reducing model complexity and increasing robustness to noise and adversarial attacks. Artificial neural networks are high-capacity models with high data and computational demands. This represents a serious problem for using these models in environments with limited computational resources. Using a differential architectural search technique, we guide the modularization of a fully-connected network into a modular multi-path network. By evaluating sampled architectures, we can establish a relation between latency and accuracy that can be used to meet a required soft balance between these conflicting measures. A related problem is reducing the complexity of neural network models while minimizing accuracy loss. CapsNet is a neural network architecture that builds on the ideas of convolutional neural networks. However, the original architecture is shallow and has wide layers that contribute significantly to its complexity. By replacing the early wide layers by parallel deep independent paths, we can significantly reduce the complexity of the model. Combining this modular architecture with max-pooling, DropCircuit regularization and a modified variant of the routing algorithm, we can achieve lower model latency with the same or better accuracy compared to the baseline. The last problem we address is the sensitivity of neural network models to random noise and to adversarial attacks, a highly disruptive form of engineered noise. Convolutional layers are the basis of state-of-the-art computer vision models and, much like other neural network layers, they suffer from sensitivity to noise and adversarial attacks. We introduce the weight map layer, a modular layer based on the convolutional layer, that can increase model robustness to noise and adversarial attacks. We conclude our work by a general discussion about the investigated relation between modularity and the addressed problems and potential future research directions

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails