Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature

Although current state-of-the-art Transformer-based solutions succeeded in a wide range for single-document NLP tasks, they still struggle to address multi-input tasks such as multi-document summarization. Many solutions truncate the inputs, thus ignoring potential summary-relevant contents, which is unacceptable in the medical domain where each information can be vital. Others leverage linear model approximations to apply multi-input concatenation, worsening the results because all information is considered, even if it is conflicting or noisy with respect to a shared background. Despite the importance and social impact of medicine, there are no ad-hoc solutions for multi-document summarization. For this reason, we propose a novel discriminative marginalized probabilistic method (DAMEN) trained to discriminate critical information from a cluster of topic-related medical documents and generate a multi-document summary via token probability marginalization. Results prove we outperform the previous state-of-the-art on a biomedical dataset for multi-document summarization of systematic literature reviews. Moreover, we perform extensive ablation studies to motivate the design choices and prove the importance of each module of our method

Geometric data understanding : deriving case specific features

There exists a tradition using precise geometric modeling, where uncertainties in data can be considered noise. Another tradition relies on statistical nature of vast quantity of data, where geometric regularity is intrinsic to data and statistical models usually grasp this level only indirectly. This work focuses on point cloud data of natural resources and the silhouette recognition from video input as two real world examples of problems having geometric content which is intangible at the raw data presentation. This content could be discovered and modeled to some degree by such machine learning (ML) approaches like deep learning, but either a direct coverage of geometry in samples or addition of special geometry invariant layer is necessary. Geometric content is central when there is a need for direct observations of spatial variables, or one needs to gain a mapping to a geometrically consistent data representation, where e.g. outliers or noise can be easily discerned. In this thesis we consider transformation of original input data to a geometric feature space in two example problems. The first example is curvature of surfaces, which has met renewed interest since the introduction of ubiquitous point cloud data and the maturation of the discrete differential geometry. Curvature spectra can characterize a spatial sample rather well, and provide useful features for ML purposes. The second example involves projective methods used to video stereo-signal analysis in swimming analytics. The aim is to find meaningful local geometric representations for feature generation, which also facilitate additional analysis based on geometric understanding of the model. The features are associated directly to some geometric quantity, and this makes it easier to express the geometric constraints in a natural way, as shown in the thesis. Also, the visualization and further feature generation is much easier. Third, the approach provides sound baseline methods to more traditional ML approaches, e.g. neural network methods. Fourth, most of the ML methods can utilize the geometric features presented in this work as additional features.Geometriassa käytetään perinteisesti tarkkoja malleja, jolloin datassa esiintyvät epätarkkuudet edustavat melua. Toisessa perinteessä nojataan suuren datamäärän tilastolliseen luonteeseen, jolloin geometrinen säännönmukaisuus on datan sisäsyntyinen ominaisuus, joka hahmotetaan tilastollisilla malleilla ainoastaan epäsuorasti. Tämä työ keskittyy kahteen esimerkkiin: luonnonvaroja kuvaaviin pistepilviin ja videohahmontunnistukseen. Nämä ovat todellisia ongelmia, joissa geometrinen sisältö on tavoittamattomissa raakadatan tasolla. Tämä sisältö voitaisiin jossain määrin löytää ja mallintaa koneoppimisen keinoin, esim. syväoppimisen avulla, mutta joko geometria pitää kattaa suoraan näytteistämällä tai tarvitaan neuronien lisäkerros geometrisia invariansseja varten. Geometrinen sisältö on keskeinen, kun tarvitaan suoraa avaruudellisten suureiden havainnointia, tai kun tarvitaan kuvaus geometrisesti yhtenäiseen dataesitykseen, jossa poikkeavat näytteet tai melu voidaan helposti erottaa. Tässä työssä tarkastellaan datan muuntamista geometriseen piirreavaruuteen kahden esimerkkiohjelman suhteen. Ensimmäinen esimerkki on pintakaarevuus, joka on uudelleen virinneen kiinnostuksen kohde kaikkialle saatavissa olevan datan ja diskreetin geometrian kypsymisen takia. Kaarevuusspektrit voivat luonnehtia avaruudellista kohdetta melko hyvin ja tarjota koneoppimisessa hyödyllisiä piirteitä. Toinen esimerkki koskee projektiivisia menetelmiä käytettäessä stereovideosignaalia uinnin analytiikkaan. Tavoite on löytää merkityksellisiä paikallisen geometrian esityksiä, jotka samalla mahdollistavat muun geometrian ymmärrykseen perustuvan analyysin. Piirteet liittyvät suoraan johonkin geometriseen suureeseen, ja tämä helpottaa luonnollisella tavalla geometristen rajoitteiden käsittelyä, kuten väitöstyössä osoitetaan. Myös visualisointi ja lisäpiirteiden luonti muuttuu helpommaksi. Kolmanneksi, lähestymistapa suo selkeän vertailumenetelmän perinteisemmille koneoppimisen lähestymistavoille, esim. hermoverkkomenetelmille. Neljänneksi, useimmat koneoppimismenetelmät voivat hyödyntää tässä työssä esitettyjä geometrisia piirteitä lisäämällä ne muiden piirteiden joukkoon

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Aleatoric Uncertainty Modelling in Regression Problems using Deep Learning

[eng] Nowadays, we live in an intrinsically uncertain world from our perspective. We do not know what will happen in the future but, to infer it, we build the so-called models. These models are abstractions of the world we live which allow us to conceive how the world works and that are, essentially, validated from our previous experience and discarded if their predictions prove to be incorrect in the future. This common scientific process of inference has several non-deterministic steps. First of all, our measuring instruments could be inaccurate. That is, the information we use a priori to know what will happen may already contain some irreducible error. Besides, our past experience in building the model could be biased (and, therefore, we would incorrectly infer the future, as the models would be based on unrepresentative data). On the other hand, our model itself may be an oversimplification of the reality (which would lead us to unrealistic generalizations). Furthermore, the overall task of inferring the future may be downright non-deterministic. This often happens when the information we have a priori to infer the future is incomplete or partial for the task to be performed (i.e. it depends on factors we cannot observe at the time of prediction) and we are, consequently, obliged to consider that what we want to predict is not a deterministic value. One way to model all of these uncertainties is through a probabilistic approach that mathematically formalizes these sources of uncertainty in order to create specific methods that capture them. Accordingly, the general aim of this thesis is to define a probabilistic approach that contributes to artificial intelligence-based systems (specifically, deep learning) becoming robust and reliable systems capable of being applied to high-risk problems, where having generic good performance is not enough but also to ensure that critical errors with high costs are avoided. In particular, the thesis shows the current divergence in the literature - when it comes to dividing and naming the different types of uncertainty - by proposing a procedure to follow. In addition, based on a real problem case arising from the industrial nature of the current thesis, the importance of investigating the last type of uncertainty is emphasized, which arises from the lack of a priori information in order to infer deterministically the future, the so-called aleatoric uncertainty. The current thesis delves into different literature models in order to capture aleatoric uncertainty using deep learning and analyzes their limitations. In addition, it proposes new state-of-the-art approaches that allow to solve the limitations exposed during the thesis. As a result of applying the aleatoric uncertainty modelling in real-world problems, the uncertainty modelling of a black box systems problem arises. Generically, a Black box system is a pre-existing predictive system which originally do not model uncertainty and where no requirements or assumptions are made about its internals. Therefore, the goal is to build a new system that wrappers the black box and models the uncertainty of this original system. In this scenario, not all previously introduced aleatoric uncertainty modelling approaches can be considered and this implies that flexible methods such as Quantile Regression ones need to be modified in order to be applied in this context. Subsequently, the Quantile Regression study brings the need to solve one critical literature problem in the QR literature, the so-called crossing quantile, which motivates the proposal of new additional models to solve it. Finally, all of the above research will be summarized in visualization and evaluation methods for the predicted uncertainty to produce uncertainty-tailored methods.[cat] Estem rodejats d’incertesa. Cada decisió que prenem té una probabilitat de sortir com un espera i, en funció d’aquesta, molts cops condicionem les nostres decisions. De la mateixa manera, els sistemes autònoms han de saber interpretar aquests escenaris incerts. Tot i això, actualment, malgrat els grans avenços en el camp de la intel·ligència artificial, ens trobem en un moment on la incapacitat d'aquests sistemes per poder identificar a priori un escenari de major risc impedeix la seva inclusió com a part de solucions que podrien revolucionar la societat tal i com la coneixem. El repte és significatiu i, per això, és essencial que aquests sistemes aprenguin a modelar i gestionar totes les fonts de la incertesa. Partint d'un enfocament probabilístic, aquesta tesi proposa formalitzar els diferents tipus d'incerteses i, en particular, centra la seva recerca en un tipus anomenada com incertesa aleatòrica, ja que va ser detectada com la principal incertesa decisiva a tractar en el problema financer original que va motivar el present doctorat industrial. A partir d'aquesta investigació, la tesi proposa nous models per millorar l'estat de l'art en la modelització de la incertesa aleatòrica, així com introdueix un nou problema, a partir d’una necessitat real industrial, que apareix quan hi ha un sistema predictiu en producció que no modela la incertesa i es vol modelar la incertesa a posteriori de forma independent. Aquest problema es denotarà com la modelització de la incertesa d'un sistema de caixa negra i motivarà la proposta de nous models especialitzats en mantenir els avantatges predictius, com ara la Regressió Quantílica (RQ), adaptant-los al problema de la caixa negra. Posteriorment, la investigació en RQ motivarà la proposta de nous models per resoldre un problema fonamental de la literatura en RQ conegut com el fenomen del creuament de quantils, que apareix quan, a l’hora de predir simultàniament diferents quantils, l’ordre entre quantils no es conserva. Finalment, tota la investigació anterior es resumirà en mètodes de visualització i avaluació de la incertesa reportada per tal de produir mètodes que mitjançant aquesta informació extra prenguin decisions més robustes