Dipartimento di Matematica e Informatica

Now showing 1 - 2 of 2

Deep Learning and Graph Theory for Brain Connectivity Analysis in Multiple Sclerosis
(Università della Calabria, 2020-01-16) Marzullo, Aldo; Leone, Nicola; Calimeri, Francesco; Terracina, Giorgio;
Multiple sclerosis (MS) is a chronic disease of the central nervous system, leading cause of nontraumatic disability in young adults. MS is characterized by inflammation, demyelination and neurodegenrative pathological processes which cause a wide range of symptoms, including cognitive deficits and irreversible disability. Concerning the diagnosis of the disease, the introduction of Magnetic Resonance Imaging (MRI) has constituted an important revolution in the last 30 years. Furthermore, advanced MRI techniques, such as brain volumetry, magnetization transfer imaging (MTI) and diffusion-tensor imaging (DTI) are nowadays the main tools for detecting alterations outside visible brain lesions and contributed to our understanding of the pathological mechanisms occurring in normal appearing white matter. In particular, new approaches based on the representation of MR images of the brain as graph have been used to study and quantify damages in the brain white matter network, achieving promising results. In the last decade, novel deep learning based approaches have been used for studying social networks, and recently opened new perspectives in neuroscience for the study of functional and structural brain connectivity. Due to their effectiveness in analyzing large amount of data, detecting latent patterns and establishing functional relationships between input and output, these artificial intelligence techniques have gained particular attention in the scientific community and is nowadays widely applied in many context, including computer vision, speech recognition, medical diagnosis, among others. In this work, deep learning methods were developed to support biomedical image analysis, in particular for the classification and the characterization of MS patients based on structural connectivity information. Graph theory, indeed, constitutes a sensitive tool to analyze the brain networks and can be combined with novel deep learning techniques to detect latent structural properties useful to investigate the progression of the disease. In the first part of this manuscript, an overview of the state of the art will be given. We will focus our analysis on studies showing the interest of DTI for WM characterization in MS. An overview of the main deep learning techniques will be also provided, along with examples of application in the biomedical domain. In a second part, two deep learning approaches will be proposed, for the generation of new, unseen, MRI slices of the human brain and for the automatic detection of the optic disc in retinal fundus images. In the third part, graph-based deep learning techniques will be applied to the study of brain structural connectivity of MS patients. Graph Neural Network methods to classify MS patients in their respective clinical profiles were proposed with particular attention to the model interpretation, the identification of potentially relevant brain substructures, and to the investigation of the importance of local graph-derived metrics for the classification task. Semisupervised and unsupervised approaches were also investigated with the aim of reducing the human intervention in the pipeline.
Towards an effective and explainable AI: studies in the biomedical domain
(Università della Calabria, 2021-07-05) Bruno, Pierangela; Greco, Gianluigi; Calimeri, Francesco
Providing accurate diagnoses of diseases and maximizing the effectiveness of treatments requires, in general, complex analyses of many clinical, omics, and imaging data. Making a fruitful use of such data is not straightforward, as they need to be properly handled and processed in order to successfully perform medical diagnosis. This is why Artificial Intelligence (AI) is largely employed in the field. Indeed, in recent years, Machine Learning (ML), and in particular Deep Learning (DL), techniques emerged as powerful tools to perform specific disease detection and classification, thus providing significant support to clinical decisions. They gained a special attention in the scientific community, especially thanks to their ability in analyzing huge amounts of data, recognizing patterns, and discovering non-trivial functional relationships between input and output. However, such approaches suffer, in general, from the lack of proper means for interpreting the choices made by the learned models, especially in the case of DL ones. This work is based on both a theoretical and methodological study of AI techniques suitable for the biomedical domain; furthermore, we put a specific focus on the practical impact on the application and adaptation of such techniques to relevant domain. In this work, ML and DL approaches have been studied and proper methods have been developed to support (i) medical imaging diagnostic and computer-assisted surgery via detection, segmentation and classification of vessels and surgical tools in intra-operative images and videos (e.g., cine-angiography), and (ii) data-driven disease classification and prognosis prediction, through a combination of data reduction, data visualization and classification of high-dimensional clinical and omics data, to detect hidden structural properties useful to investigate the progression of the disease. In particular, we focus on defining a novel approach for automated assessment of pathological conditions, identifying latent relationships in different domains and supporting healthcare providers in finding the most appropriate preventive interventions and therapeutic strategies. Furthermore, we propose a study about the analysis of the internal processes performed by the artificial networks during classification tasks, with the aim to provide a AI-based model explainability. This manuscript is presented in four parts, each focusing on a special aspect of DL techniques and offering different examples of their application in the biomedical domain. In the first part we introduce clinical and omics data along with the popular processing methods to improve the analyses; we also provide an overview of the main DL techniques and approaches aimed at performing disease prediction and prevention and at identifying bio-markers via biomedical data and images. In the second part we describe how we applied DL techniques to perform the segmentation of vessels in the ilio-femoral images. Furthermore, we propose a combination of multi-instance segmentation network and optical flow to solve the multiinstance segmentation and detection tasks in endoscopic images. In the third part a combination of data reduction and data visualization techniques is proposed for the reduction of clinical and omics data and their visualization into images, with the aim of performing DL-based classification. Furthermore, we present a ML-based approach to develop a risk model for class prediction from high-dimensional gene expression data, for the purpose of identifying a subset of genes that may influence the survival rate of specific patients. Eventually, in the fourth part we provide a study on the behaviour of AI-based systems during classification tasks, such as image-based disease classification, which is a widely studied topic in the recent years; more in detail, we show how DL-based systems can be studied with the aim of identifying the most relevant elements involved in the training processes and validating the network’s decisions, and possibly the clinical treatment and recommendation.

Dipartimento di Matematica e Informatica - Tesi di Dottorato

Browse

Filters

Settings

Sort By

Results per page

Search Results