51 research outputs found

    Unsupervised online clustering and detection algorithms using crowdsourced data for malaria diagnosis

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    © . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Crowdsourced data in science might be severely error-prone due to the inexperience of annotators participating in the project. In this work, we present a procedure to detect specific structures in an image given tags provided by multiple annotators and collected through a crowdsourcing methodology. The procedure consists of two stages based on the Expectation–Maximization (EM) algorithm, one for clustering and the other one for detection, and it gracefully combines data coming from annotators with unknown reliability in an unsupervised manner. An online implementation of the approach is also presented that is well suited to crowdsourced streaming data. Comprehensive experimental results with real data from the MalariaSpot project are also included.Peer ReviewedPreprin

    Assessing emphysema in CT scans of the lungs:Using machine learning, crowdsourcing and visual similarity

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    EM based algorithms for Malaria diagnose via crowdsourcing

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    We live in a world in which medicine and technology are more united than ever. That is why in the last few years, lots of research groups initially dedicated to the development of technologies, have started to investigate in a field in which progresses are needed in order to protect the humanity against diseases, an this field is the medicine one. This work, following this trend, is focused on one of the diseases that affects the bast majority of tropical countries, Malaria. Along this final Degree Thesis, this disease will be the center of the work, firstly trying to sensitize the reader about its importance and after that, once the objectives have been defined, develop signal processing techniques and algorithms that in the end will count and detect malaria parasites via crowdsourcing, system that is explained in the Introduction chapter.Vivimos en un mundo en el que medicina y tecnología cada vez van más de la mano. Es por ello que durante los últimos años, muchos grupos de investigación inicialmente dedicados al desarrollo de tecnología, han desembarcado en un campo en el cual se necesitan avances para poder proteger al ser humano de enfermedades, es decir, el campo de la medicina. Este proyecto, siguiendo esta tendencia, se centra en una de las enfermedades que más afecta a países de zonas tropicales, la Malaria. A lo largo de este trabajo final de grado se hablará de esta enfermedad y se podrá sensibilizar al lector de su importancia. Una vez se han fijado los objetivos, se desarrollaran técnicas y algoritmos de procesado de señal cuya finalidad será la de contar y detectar parásitos de malaria mediante crowdsourcing, sistema explicado en la introducción.Vivim en un món el qual medicina i tecnologia cada cop estan més units. És per això que durant els últims anys, molts grups de recerca que inicialment es dedicaven al desenvolupament de tecnologia, s'han submergit en un camp en el qual es necessiten mes avenços per poder protegir l'ésser humà d'enfermetats, és a dir, el món de la medicina. Aquest treball, seguint aquesta tendència, es centra en una de les enfermetats que més afecta a països de zones tropicals, la Malaria. Durant aquest projecte de final de grau es parlarà sobre aquesta enfermetat i es podrà sensibilitzar el lector de la seva importancia. Un cop els objectius del treball han estat fixats es desenvoluparan tècniques i algorismes de processament del senyal la finalitat dels qual serà la de contar i detectar paràsits de malaria mitjançant crowdsourcing, sistema explicat a la introducció

    Unsupervised ensemble classification with correlated decision agents

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    © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Decision-making procedures when a set of individual binary labels is processed to produce a unique joint decision can be approached modeling the individual labels as multivariate independent Bernoulli random variables. This probabilistic model allows an unsupervised solution using EM-based algorithms, which basically estimate the distribution model parameters and take a joint decision using a Maximum a Posteriori criterion. These methods usually assume that individual decision agents are conditionally independent, an assumption that might not hold in practical setups. Therefore, in this work we formulate and solve the decision-making problem using an EM-based approach but assuming correlated decision agents. Improved performance is obtained on synthetic and real datasets, compared to classical and state-of-the-art algorithms.Peer ReviewedPostprint (author's final draft

    On the information theory of clustering, registration, and blockchains

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    Progress in data science depends on the collection and storage of large volumes of reliable data, efficient and consistent inference based on this data, and trusting such computations made by untrusted peers. Information theory provides the means to analyze statistical inference algorithms, inspires the design of statistically consistent learning algorithms, and informs the design of large-scale systems for information storage and sharing. In this thesis, we focus on the problems of reliability, universality, integrity, trust, and provenance in data storage, distributed computing, and information processing algorithms and develop technical solutions and mathematical insights using information-theoretic tools. In unsupervised information processing we consider the problems of data clustering and image registration. In particular, we evaluate the performance of the max mutual information method for image registration by studying its error exponent and prove its universal asymptotic optimality. We further extend this to design the max multiinformation method for universal multi-image registration and prove its universal asymptotic optimality. We then evaluate the non-asymptotic performance of image registration to understand the effects of the properties of the image transformations and the channel noise on the algorithms. In data clustering we study the problem of independence clustering of sources using multivariate information functionals. In particular, we define consistent image clustering algorithms using the cluster information, and define a new multivariate information functional called illum information that inspires other independence clustering methods. We also consider the problem of clustering objects based on labels provided by temporary and long-term workers in a crowdsourcing platform. Here we define budget-optimal universal clustering algorithms using distributional identicality and temporal dependence in the responses of workers. For the problem of reliable data storage, we consider the use of blockchain systems, and design secure distributed storage codes to reduce the cost of cold storage of blockchain ledgers. Additionally, we use dynamic zone allocation strategies to enhance the integrity and confidentiality of these systems, and frame optimization problems for designing codes applicable for cloud storage and data insurance. Finally, for the problem of establishing trust in computations over untrusting peer-to-peer networks, we develop a large-scale blockchain system by defining the validation protocols and compression scheme to facilitate an efficient audit of computations that can be shared in a trusted manner across peers over the immutable blockchain ledger. We evaluate the system over some simple synthetic computational experiments and highlights its capacity in identifying anomalous computations and enhancing computational integrity

    A Human-Machine Framework for the Classification of Phonocardiograms

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    In this thesis, we present and evaluate a framework for combining machine learning algo- rithms, crowd workers, and experts in the classification of heart sound recordings. The development of a hybrid human-machine framework for heart sound recordings is moti- vated by the past success in utilizing human computation to solve problems in medicine as well as the use of human-machine frameworks in other domains. We describe the methods that decide when and how to escalate the analysis of heart sound recordings to different resources and incorporate their decision into a final classification. We present and discuss the results of the framework which was tested with a number of different machine classi- fiers and a group of crowd workers from Amazon’s Mechanical Turk. We also provide an evaluation of how crowd workers perform in various different heart sound analysis tasks, and how they compare with machine classifiers. In addition, we investigate how machine and human analysis are effected by different types of heart sounds and provide a strategy for involving experts when these methods are uncertain. We conclude that the use of a hybrid framework is a viable method for heart sound classification

    Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review

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    © 2020 Elsevier Ltd. All rights reserved.Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.Peer reviewe

    Women in Artificial intelligence (AI)

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    This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI
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