PUBLICATIONS

ABSTRACT

Medical images are an essential input for the timely diagnosis of pathologies. Despite its wide use in the area, searching for images that can reveal valuable information to support decision-making is difficult and expensive. However, the possibilities that open when making large repositories of images available for search by content are unsuspected. We designed a content-based image retrieval system for medical imaging, which reduces the gap between access to information and the availability of useful repositories to meet these needs. The system operates on the principle of query-by-example, in which users provide medical images, and the system displays a set of related images. Unlike metadata match-driven searches, our system drives content-based search. This allows the system to conduct searches on repositories of medical images that do not necessarily have complete and curated metadata. We explore our system’s feasibility in computational tomography (CT) slices for SARS-CoV-2 infection (COVID-19), showing that our proposal obtains promising results, advantageously comparing it with other search methods.

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Los bots tienen un nefasto efecto en la diseminación de información engañosa o tendenciosa en redes sociales [1]. Su objetivo es amplificar la alcanzabilidad de campañas, transformando artificialmente mensajes en tendencias. Para ello, las cuentas que dan soporte a campañas se hacen seguir por cuentas manejadas por algoritmos. Muchas de las cuentas que siguen a personajes de alta connotación pública son bots, las cuales entregan soporte a sus mensajes con likes y retweets. Cuando estos mensajes muestran un inusitado nivel de reacciones, se transforman en tendencias, lo cual aumenta aún más su visibilidad. Al transformarse en tendencias, su influencia en la red crece, produciendo un fenómeno de bola de nieve.

ABSTRACT

Current language models are usually trained using a self-supervised scheme, where the main focus is learning representations at the word or sentence level. However, there has been limited progress in generating useful discourse-level representations. In this work, we propose to use ideas from predictive coding theory to augment BERT-style language models with a mechanism that allows them to learn suitable discourse-level representations. As a result, our proposed approach is able to predict future sentences using explicit top-down connections that operate at the intermediate layers of the network. By experimenting with benchmarks designed to evaluate discourse-related knowledge using pre-trained sentence representations, we demonstrate that our approach improves performance in 6 out of 11 tasks by excelling in discourse relationship detection.

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ABSTRACT

Medical imaging is essential nowadays throughout medical education, research, and care. Accordingly, international efforts have been made to set large-scale image repositories for these purposes. Yet, to date, browsing of large-scale medical image repositories has been troublesome, time-consuming, and generally limited by text search engines. A paradigm shift, by means of a query-by-example search engine, would alleviate these constraints and beneficially impact several practical demands throughout the medical field. The current project aims to address this gap in medical imaging consumption by developing a content-based image retrieval (CBIR) system, which combines two image processing architectures based on deep learning. Furthermore, a first-of-its-kind intelligent visual browser was designed that interactively displays a set of imaging examinations with similar visual content on a similarity map, making it possible to search for and efficiently navigate through a large-scale medical imaging repository, even if it has been set with incomplete and curated metadata. Users may, likewise, provide text keywords, in which case the system performs a content- and metadata-based search. The system was fashioned with an anonymizer service and designed to be fully interoperable according to international standards, to stimulate its integration within electronic healthcare systems and its adoption for medical education, research and care. Professionals of the healthcare sector, by means of a self-administered questionnaire, underscored that this CBIR system and intelligent interactive visual browser would be highly useful for these purposes. Further studies are warranted to complete a comprehensive assessment of the performance of the system through case description and protocolized evaluations by medical imaging specialists.

ABSTRACT

Valuable and timely information about crisis situations such as natural disasters, can be rapidly obtained from user-generated content in social media. This has created an emergent research field that has focused mostly on the problem of filtering and classifying potentially relevant messages during emergency situations. However, we believe important insight can be gained from studying online communications during disasters at a more comprehensive level. In this sense, a higher-level analysis could allow us to understand if there are collective patterns associated to certain characteristics of events. Following this motivation, we present a novel comparative analysis of 41 real-world crisis events. This analysis is based on textual and linguistic features of social media messages shared during these crises. For our comparison we considered hazard categories (i.e., human-induced and natural crises) as well as subcategories (i.e., intentional, accidental and so forth). Among other things, our results show that using only a small set of textual features, we can differentiate among types of events with 75% accuracy. Indicating that there are clear patterns in how people react to different extreme situations, depending on, for example, whether the event was triggered by natural causes or by human action. These findings have implications from a crisis response perspective, as they will allow experts to foresee patterns in emerging situations, even if there is no prior experience with an event of such characteristics.1

ABSTRACT

Automatic hate speech detection in online social networks is an important open problem in Natural Language Processing (NLP). Hate speech is a multidimensional issue, strongly dependant on language and cultural factors. Despite its relevance, research on this topic has been almost exclusively devoted to English. Most supervised learning resources, such as labeled datasets and NLP tools, have been created for this same language. Considering that a large portion of users worldwide speak in languages other than English, there is an important need for creating efficient approaches for multilingual hate speech detection. In this work we propose to address the problem of multilingual hate speech detection from the perspective of transfer learning. Our goal is to determine if knowledge from one particular language can be used to classify other language, and to determine effective ways to achieve this. We propose a hate specific data representation and evaluate its effectiveness against general-purpose universal representations most of which, unlike our proposed model, have been trained on massive amounts of data. We focus on a cross-lingual setting, in which one needs to classify hate speech in one language without having access to any labeled data for that language. We show that the use of our simple yet specific multilingual hate representations improves classification results. We explain this with a qualitative analysis showing that our specific representation is able to capture some common patterns in how hate speech presents itself in different languages. Our proposal constitutes, to the best of our knowledge, the first attempt for constructing multilingual specific-task representations. Despite its simplicity, our model outperformed the previous approaches for most of the experimental setups. Our findings can orient future solutions toward the use of domain-specific representations.

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