Computer aided diagnosis system for breast cancer using deep learning.

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists and doctors for medical imaging analysis, which has remained the essence of the visual representation that is used to construct the final observation and diagnosis. Medical research in cancerology and oncology has been recently blended with the knowledge gained from computer engineering and data science experts. In this context, an automatic assistance or commonly known as Computer-aided Diagnosis (CAD) system has become a popular area of research and development in the last decades. As a result, the CAD systems have been developed using multidisciplinary knowledge and expertise and they have been used to analyze the patient information to assist clinicians and practitioners in their decision-making process. Treating and preventing cancer remains a crucial task that radiologists and oncologists face every day to detect and investigate abnormal tumors. Therefore, a CAD system could be developed to provide decision support for many applications in the cancer patient care processes, such as lesion detection, characterization, cancer staging, tumors assessment, recurrence, and prognosis prediction. Breast cancer has been considered one of the common types of cancers in females across the world. It was also considered the leading cause of mortality among women, and it has been increased drastically every year. Early detection and diagnosis of abnormalities in screened breasts has been acknowledged as the optimal solution to examine the risk of developing breast cancer and thus reduce the increasing mortality rate. Accordingly, this dissertation proposes a new state-of-the-art CAD system for breast cancer diagnosis that is based on deep learning technology and cutting-edge computer vision techniques. Mammography screening has been recognized as the most effective tool to early detect breast lesions for reducing the mortality rate. It helps reveal abnormalities in the breast such as Mass lesion, Architectural Distortion, Microcalcification. With the number of daily patients that were screened is continuously increasing, having a second reading tool or assistance system could leverage the process of breast cancer diagnosis. Mammograms could be obtained using different modalities such as X-ray scanner and Full-Field Digital mammography (FFDM) system. The quality of the mammograms, the characteristics of the breast (i.e., density, size) or/and the tumors (i.e., location, size, shape) could affect the final diagnosis. Therefore, radiologists could miss the lesions and consequently they could generate false detection and diagnosis. Therefore, this work was motivated to improve the reading of mammograms in order to increase the accuracy of the challenging tasks. The efforts presented in this work consists of new design and implementation of neural network models for a fully integrated CAD system dedicated to breast cancer diagnosis. The approach is designed to automatically detect and identify breast lesions from the entire mammograms at a first step using fusion models’ methodology. Then, the second step only focuses on the Mass lesions and thus the proposed system should segment the detected bounding boxes of the Mass lesions to mask their background. A new neural network architecture for mass segmentation was suggested that was integrated with a new data enhancement and augmentation technique. Finally, a third stage was conducted using a stacked ensemble of neural networks for classifying and diagnosing the pathology (i.e., malignant, or benign), the Breast Imaging Reporting and Data System (BI-RADS) assessment score (i.e., from 2 to 6), or/and the shape (i.e., round, oval, lobulated, irregular) of the segmented breast lesions. Another contribution was achieved by applying the first stage of the CAD system for a retrospective analysis and comparison of the model on Prior mammograms of a private dataset. The work was conducted by joining the learning of the detection and classification model with the image-to-image mapping between Prior and Current screening views. Each step presented in the CAD system was evaluated and tested on public and private datasets and consequently the results have been fairly compared with benchmark mammography datasets. The integrated framework for the CAD system was also tested for deployment and showcase. The performance of the CAD system for the detection and identification of breast masses reached an overall accuracy of 97%. The segmentation of breast masses was evaluated together with the previous stage and the approach achieved an overall performance of 92%. Finally, the classification and diagnosis step that defines the outcome of the CAD system reached an overall pathology classification accuracy of 96%, a BIRADS categorization accuracy of 93%, and a shape classification accuracy of 90%. Results given in this dissertation indicate that our suggested integrated framework might surpass the current deep learning approaches by using all the proposed automated steps. Limitations of the proposed work could occur on the long training time of the different methods which is due to the high computation of the developed neural networks that have a huge number of the trainable parameters. Future works can include new orientations of the methodologies by combining different mammography datasets and improving the long training of deep learning models. Moreover, motivations could upgrade the CAD system by using annotated datasets to integrate more breast cancer lesions such as Calcification and Architectural distortion. The proposed framework was first developed to help detect and identify suspicious breast lesions in X-ray mammograms. Next, the work focused only on Mass lesions and segment the detected ROIs to remove the tumor’s background and highlight the contours, the texture, and the shape of the lesions. Finally, the diagnostic decision was predicted to classify the pathology of the lesions and investigate other characteristics such as the tumors’ grading assessment and type of the shape. The dissertation presented a CAD system to assist doctors and experts to identify the risk of breast cancer presence. Overall, the proposed CAD method incorporates the advances of image processing, deep learning, and image-to-image translation for a biomedical application

Évaluation de six propositions de réforme de la TVA sur données microéconomiques

Nous utilisons la méthodologie de King pour décrire en termes de variation de bien-être les effets de six propositions de réforme de la TVA pour la France, parmi lesquelles figure celle de la Commission Européenne.Un résultat constant est le faible impact de ces réformes sur l’inégalité de la distribution des niveaux de bien-être, mettant une fois encore en évidence les limites du pouvoir redistributif de la TVA.La comparaison avec des études antérieures portant sur le passage à un taux unique et sur la réforme de 1982 indique une étonnante robustesse des résultats à des modifications d'éléments de la spécification tels que le nombre et la nature des biens considérés.La réforme proposée par la Commission Européenne apparaît, dans le cadre d’analyse partielle choisi ici, comme à la fois peu efficace et légèrement inégalitaire.Enfin, l’analyse indique l’importance du calcul d’écart-types pour les mesures de variation de bien-être estimées, en fournissant à la fois des exemples d’effets importants mais non significatifs et d’effets en apparence minimes, mais bien déterminés.We use King's methodology to describe the welfare effects of six VAT reform proposals for France, among which the proposal of the European Commission.A constant result is the weak impact of the reforms on the inequality of the distribution of welfare levels, pointing once more to the limits of redistribution through VAT.A comparison with previous studies, concerning the move to a unique VAT rate and the 1982 reform, shows the results to be surprisingly robust to changes in some aspects of the model specification, such as the number and nature of the goods considered.Within the partial analysis framework adopted here, the proposal of the European Commission appears both as inefficient ans slightly inegalitarian.Finally, the analysis demonstrates the importance of providing standard errors for the estimated welfare measures by providing examples of both large insignifiant measures, and small but well determined ones

Évaluation de six propositions de réforme de la TVA sur données microéconomiques

We use King's methodology to describe the welfare effects of six VAT reform proposals for France, among which the proposal of the European Commission. Nous utilisons la méthodologie de King pour décrire en termes de variation de bien-être les effets de six propositions de réforme de la TVA pour la France, parmi lesquelles figure celle de la Commission Européenne.

Echo State Networks: analysis, training and predictive control

The goal of this paper is to investigate the theoretical properties, the training algorithm, and the predictive control applications of Echo State Networks (ESNs), a particular kind of Recurrent Neural Networks. First, a condition guaranteeing incremetal global asymptotic stability is devised. Then, a modified training algorithm allowing for dimensionality reduction of ESNs is presented. Eventually, a model predictive controller is designed to solve the tracking problem, relying on ESNs as the model of the system. Numerical results concerning the predictive control of a nonlinear process for pH neutralization confirm the effectiveness of the proposed algorithms for the identification, dimensionality reduction, and the control design for ESNs.Comment: 6 pages,5 figures, submitted to European Control Conference (ECC

Les déterminants de la publication volontaire d'informations sociales : cas des entreprises tunisiennes

Cette recherche étudie les pratiques de publication volontaire des informations sociales des entreprises tunisiennes cotées, ainsi que les facteurs susceptibles d'influencer le niveau de publication dans les rapports annuels. Nous proposons, tout d'abord, une revue des recherches antérieures. Ensuite, nous essayons d'identifier les déterminants de la publication volontaire des informations sociales, dans la cadre de la théorie des parties prenantes, de celle de la légitimité et de la théorie politico- contractuelle. La répartition de la quantité totale des informations publiées entre les quatre catégories d'informations (Répartition et évolution des effectifs, Informations sur les politiques de recrutement et de rémunération, formation, informations sur les conditions générales de travail) nous a permis de trouver que les entreprises tunisiennes tendent à publier davantage et à mieux expliquer la seconde catégorie .La vérification empirique des hypothèses formulées relatives aux déterminants du niveau de publication d'informations sociales a distingué l'actionnariat de l'Etat, la performance économique et l'âge comme des facteurs explicatifs et significatifs de ce niveau

Une nouvelle méthode de Web Usage Mining basée sur une analyse sémiotique du comportement de navigation

International audienceL’objectif de nos travaux est de proposer une méthode d’analyse automatique du comportement des utilisateurs à des fins de prédiction de leur propension à réaliser une action suggérée. Nous proposons dans cet article une nouvelle méthode de Web Usage Mining basée sur une étude sémiotique des styles perceptifs, considérant l’expérience de l’utilisateur comme élément déterminant de sa réaction à une sollicitation. L’étude de ces styles nous a amené à définir de nouveaux indicateurs (des descripteurs sémiotiques) introduisant un niveau supplémentaire à l’approche sémantique d’annotation des sites. Nous proposons ensuite un modèle neuronal adapté au traitement de ces nouveaux indicateurs. Nous expliquerons en quoi le modèle proposé est le plus pertinent pour traiter ces informations

The Impact Of Audit Committee Multiple-Directorships On Earnings Management: Evidence From France

The aim of this paper is to examine the relationship between Audit Committee Multiple-Directorships and earnings management. Precisely, we empirically investigate the effect of the multiple directorships held by audit committee directors on the level of earnings management of listed French companies. Our investigation has been achieved on a sample of 88 non financial French listed firms that belong to the SBF 120 index, for the financial year 2008. The results suggest that the accumulation of several outside directorships by audit committee members may lead to a higher degree of earnings management, as measured by the magnitude of discretionary accruals. Therefore, our findings show that audit committee can’t provide effective monitoring of earnings management when its members held many additional outside directorships

Model-Independent Predictions for Low Energy Isoscalar Heavy Baryon Observables in the Combined Heavy Quark and Large NcN_c Expansion

Model-independent predictions for excitation energies, semileptonic form factors and electromagnetic decay rates of isoscalar heavy baryons and their low energy excited states are discussed in terms of the combined heavy quark and large $N_c$ expansion. At leading order, the observables are completely determined in terms of the known excitation energy of the first excited state of $\Lambda_c$. At next-to-leading order in the combined expansion all heavy baryon observables can be expressed in a model-independent way in terms of two experimentally measurable quantities. We list predictions at leading and next-to-leading order.Comment: 7 pages, LaTe