1,721,004 research outputs found
Study and Design of Deep Learning Computer-Aided Diagnosis Systems Based on Biomedical Images and Signals
No full textThis Ph.D. thesis aims to describe the research works conducted for the design, the development and the evaluation of innovative Computer-Aided Diagnosis (CAD) systems based on machine learning and deep learning techniques. Several CAD solutions were developed in different medical applications trying to ensure, when possible, three main CAD requirements: improve clinicians performance, reduce or at least not increase clinicians time and integrate the CAD solution in standard procedures. The proposed applications involved images and signals processing; the firsts required the use of different deep learning models to face classification, detection and segmentation problems, while the latter allowed to investigate machine learning as signal processing technique for movement disorder analysis and for a more speculative research in the rehabilitation field. In order to properly validate the proposed algorithms, all the methodologies were applied on real data provided by clinicians, public datasets or specific acquisitions.
Potentialities, challenges and drawbacks about deep learning for medical imaging analysis are discussed in two medical fields, digital pathology and radiology, and complete pipelines are proposed to accomplish three clinical practices: global glomerulosclerosis analysis for Chronic Kidney Disease evaluation, kidneys volume analysis for Autosomal Dominant Polycystic Kidney Disease evaluation and organs segmentation for generic volume quantification. Each study case aims to identify and overcome the limitation of classical image processing techniques, and paves the way towards the clinical use of CAD systems based on deep learning. A second part of this thesis focuses on machine learning and deep learning for signals processing; deep neural networks were investigated for movement disorders analysis and a particular neural model for surface electromyography analysis has been proposed for the evaluation of complex muscle activation patterns, useful in the rehabilitation field. The developed solutions for signals and images processing, were compared with literature standards and, if possible, a personalised classical pipelines has been proposed and customised to face each clinical challenge.
The thesis is divided into six chapters. The first chapter provides an introduction about the reference context. The following chapter two describes the state of the art about traditional CAD systems based on conventional machine learning algorithms, and the novelty that deep learning techniques bring to CADs and medical practices; description of the main convolutional neural network models and autoencoders, and literature about the application of deep learning and machine learning to the concerned medical fields are reported. Chapters three, four and five report the original contribution about the application of deep learning and machine learning techniques to the two types of medical data: images and signals; in detail, chapter three reports the applications in the clinical areas of digital pathology and radiology, focusing on the development of full pipelines based on image analysis; chapter four shows a more speculative research work for signal processing, focusing on the application of undercomplete autoencoders for surface electromyography analysis; chapter five reports the applications of deep neural networks for diseases assessment and grading in subjects affected by movement disorders. The analysed study cases and the contributions reported in this thesis were compared with standard processing techniques ad-hoc developed. Finally, the conclusions about the research works and proposals for future researches are reported in chapter six
Computational imaging for precision medicine: the emergence of radiomics, pathomics and deep learning
No full textThe purpose of this Ph.D. thesis is to illustrate the research works carried out during the conceptualization, design, implementation, and evaluation of novel Clinical Decision Support Systems (CDSSs) based on Radiomics, Pathomics and Deep Learning (DL) techniques.
CDSSs can be effective systems for implementing Precision Medicine into clinical practice since they permit the objective and repeatable evaluation of patients. Precision Medicine can enable the improvement of the healthcare system by employing a personal healthcare process for the health status of an individual patient, which evolves in a unique way.
The methodologies concerning CDSSs were developed with different underlying goals: improvement of the clinical results, availability and usability of the method, and feasibility of the integration into the routine clinical practice. The applications considered span from Radiology to Digital Pathology. Tasks under consideration in Medical Imaging applications, from a computer vision perspective, concerned object detection, instance segmentation, semantic segmentation, color normalization, and characterization and classification of regions of interest. Data under consideration were either provided by local hospitals or obtained from public repositories. Validation of the developed systems has been done in accordance with the physicians. Moreover, the explainability of the realized systems has been investigated, by analyzing features' structure or by means of perceptive saliency maps.
In the aforementioned scenario, the main purpose of this thesis is to develop new systems based on Deep Learning, Radiomics and Pathomics for the processing and analysis of medical images.
Computational Imaging is a promising methodology to incorporate in the framework of Precision Medicine. Indeed, it creates the possibility to characterize the lesions in large datasets of images belonging to Radiology and Digital Pathology domains in an effective way, offering a personalized evaluation of the patient.
Merits and shortcomings regarding DL in the field of Medical Imaging have been investigated for applications in Radiology and Digital Pathology. Technical contributions include devising novel algorithms, improving existing workflows, and assembling complex CDSSs by combining in an original and effective way different techniques proposed in the literature.
In the Radiology domain, the following tasks have been tackled for what concerns applications related to Image-guided Surgery (IGS): liver segmentation, including also the classification into anatomical segments; vertebrae segmentation and identification; prostate segmentation and registration for image fusion.
Radiomics has been exploited for characterizing lung lesions in COVID-19 patients, in order to discover a prognostic signature for those with a higher risk of developing pulmonary thromboembolism.
With regard to Digital Pathology, applications included colorectal cancer (CRC) tissue classification; hematoxylin and eosin (H&E) stain color normalization; nuclei segmentation and detection; glomeruli lesions classifications according to Oxford score for IgA nephropathy patients. These automatic pipelines for histological data analysis can enable Pathomics, allowing the objective quantification and evaluation of tissue patterns.
The developed solutions in all these scenarios were put in comparison with state-of-the-art approaches proposed in the literature, and were validated with physicians when possible. In many cases, data have also been collected from local institutions.
This thesis work is organized into five chapters.
Chapter 1 introduces the objective and the technical contribution of the thesis.
Chapter 2 describes the state-of-the-art in all the considered clinical scenarios, with a particular focus on Radiology and Digital Pathology, encompassing emerging trends such as Radiomics and Pathomics.
Chapter 3 describes the contributions proposed in the Radiology field. In particular, IGS applications concern liver segmentation and classification into segments, vertebrae segmentation and identification, and prostate segmentation and registration. Also, a Radiomics-based analysis of lung lesions of patients diagnosed with COVID-19 is presented.
Chapter 4 presents the contributions proposed in the field of Digital Pathology, concerning tissue segmentation, normalization and classification, and detection of objects of interest, such as nuclei of cells.
Lastly, final remarks and considerations for future works are drawn in Chapter 5
Nonlinear effects in finite elements analysis of colorectal surgical clamping
Full text linkMinimal Invasive Surgery (MIS) is a procedure that has increased its applications in past few years in different types of surgeries. As number of application fields are increasing day by day, new issues have been arising. In particular, instruments must be inserted through a trocar to access the abdominal cavity without capability of direct manipulation of tissues, so a loss of sensitivity occurs. Generally speaking, the student of medicine or junior surgeons need a lot of practice hours before starting any surgical procedure, since they have to difficulty in acquiring specific skills (hand–eye coordination among others) for this type of surgery. Here is what the surgical simulator present a promising training method using an approach based on Finite Element Method (FEM).
The use of continuum mechanics, especially Finite Element Analysis (FEA) has gained an extensive application in medical field in order to simulate soft tissues. In particular, colorectal simulations can be used to understand the interaction between colon and the surrounding tissues and also between colon and instruments. Although several works have been introduced considering small displacements, FEA applied to colorectal surgical procedures with large displacements is a topic that asks for more investigations. This work aims to investigate how FEA can describe non-linear effects induced by material properties and different approximating geometries, focusing as test-case application colorectal surgery. More in detail, it shows a comparison between simulations that are performed using both linear and hyperelastic models. These different mechanical behaviours are applied on different geometrical models (planar, cylindrical, 3D-SS and a real model from digital acquisitions 3D-S) with the aim of evaluating the effects of geometric non-linearity. Final aim of the research is to provide a preliminary contribution to the simulation of the interaction between surgical instrument and colon tissues with multi-purpose FEA in order to help the preliminary set-up of different bioengineering tasks like force-contact evaluation or approximated modelling for virtual reality (surgical simulations).
In particular, the contribution of this work is focused on the sensitivity analysis of the nonlinearities by FEA in the tissue-tool interaction through an explicit FEA solver.
By doing in this way, we aim to demonstrate that the set-up of FEA computational surgical tools may be simplified in order to provide assistance to non-expert FEA engineers or medicians in more precise way of using FEA tools
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A multi scale analytic hierarchy methodology for technology assessment : a case study on spine surgery
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Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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