1,720,969 research outputs found
Utilizzo del Deep Learning nella Analisi di Immagini Dermoscopiche
No full textIn medicina, innumerevoli tecniche di acquisizione di immagini impiegate come strumento per eseguire diagnosi, dalla microscopia alla risonanza magnetica (MRI). Questa comune pratica ha prodotto una grande opportunità per gli algoritmi di visione artificiale di trovare modi per eseguire analisi automatizzate di immagini mediche. In particolar modo, dopo il rivoluzionario successo di AlexNet nel 2012, il deep learning è diventato un elemento vitale nella ricerca sulle immagini mediche. Più precisamente, le reti neurali convoluzionali (CNN) sono state adottate con successo per affrontare una grande varietà di problemi come la classificazione e la generazione di immagini, o l’identificazione e la segmentazione di oggetti all’interno di esse.
Questa tesi è una raccolta di applicazioni di deep learning su immagini dermoscopiche. La dermoscopia è una forma di microscopia in vivo della superficie cutanea, eseguita utilizzando lenti di ingrandimento di alta qualità e una potente fonte di luce, che mitiga il riflesso superficiale della pelle con lo scopo di migliorare la visibilità della pigmentazione della lesione. Tuttavia, per utilizzare a pieno questo approccio di imaging non invasivo, è necessaria un'analisi approfondita dell'immagine da parte di dermatologi esperti, e la comunità scientifica ha quindi impiegato grandi risorse per la creazione di strumenti automatici che possano assistere l'analisi di immagini dermoscopiche. Affrontiamo per prima la segmentazione delle lesioni cutanee, mediante una nuova tecnica di data augmentation e una strategia di insieme diversificata. L'obiettivo finale dell'analisi delle immagini dermoscopiche è la classificazione delle lesioni, per la quale sviluppiamo un approccio che ha ottenuto il terzo miglior risultato nella sfida globale ISIC 2019. Inoltre, affrontiamo uno dei principali svantaggi degli algoritmi di deep learning, la loro bassa interpretabilità, utilizzando un recupero di immagini basato sul contenuto per assistere il processo di diagnosi sia di dermatologi esperti che di principianti. Infine, cerchiamo di determinare quali caratteristiche siano prese in considerazione dagli algoritmi di classificazione automatica.Countless different imaging acquisition techniques are employed by medical practitioners as a tool to perform diagnosis, ranging from microscopy to Magnetic Resonance Imaging (MRI). This common practice produced a great opportunity for computer vision algorithms to find ways to perform automated analysis on medical images. In particular, after the groundbreaking success of AlexNet in 2012, deep learning has become a vital element in medical imaging research. Above all, Convolutional Neural Networks (CNNs) have been successfully adopted to perform a great variety of tasks such as image segmentation, classification, detection, and generation.
This thesis is a collection of deep learning applications for dermoscopic images analysis. Dermoscopy is a form of in-vivo skin surface microscopy performed using high quality magnifying lenses and a powerful light source to mitigate the surface reflection of the skin, to enhance the visibility of the pigmentation of the lesion. However, to fully make use of this non-invasive imaging approach, a thorough image analysis must be performed by expert clinicians, and therefore many efforts have been given in recent years towards the creation of tools to assist physicians in the analysis of dermoscopic images. We start by approaching lesion segmentation, by means of a novel data augmentation technique and a diverse ensemble strategy. The final goal of dermoscopic images analysis is skin lesion classification, for which we develop an approach that achieved the third best result in the 2019 ISIC global challenge. Moreover, we address one of the main drawbacks of deep learning algorithms, their low interpretability, by using content-based image retrieval to assist the diagnosis process of both expert and novice practitioners and, finally, by trying to determine which characteristics are taken into account by autonomous classification algorithms
Long-Range 3D Self-Attention for MRI Prostate Segmentation
Full text linkThe problem of prostate segmentation from Magnetic Resonance Imaging (MRI) is an intense research area, due to the increased use of MRI in the diagnosis and treatment planning of prostate cancer. The lack of clear boundaries and huge variation of texture and shapes between patients makes the task very challenging, and the 3D nature of the data makes 2D segmentation algorithms suboptimal for the task.
With this paper, we propose a novel architecture to fill the gap between the most recent advances in 2D computer vision and 3D semantic segmentation. In particular, the designed model retrieves multi-scale 3D features with dilated convolutions and makes use of a self-attention transformer to gain a global field of view. The proposed Long-Range 3D Self-Attention block allows the convolutional neural network to build significant features by merging together contextual information collected at various scales. Experimental results show that the proposed method improves the state-of-the-art segmentation accuracy on MRI prostate segmentation
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
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
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
A Cone Beam Computed Tomography Annotation Tool for Automatic Detection of the Inferior Alveolar Nerve Canal
Full text linkIn recent years, deep learning has been employed in several medical fields, achieving impressive results. Unfortunately, these algorithms require a huge amount of annotated data to ensure the correct learning process. When dealing with medical imaging, collecting and annotating data can be cumbersome and expensive. This is mainly related to the nature of data, often three-dimensional, and to the need for well-trained expert technicians. In maxillofacial imagery, recent works have been focused on the detection of the Inferior Alveolar Nerve (IAN), since its position is of great relevance for avoiding severe injuries during surgery operations such as third molar extraction or implant installation. In this work, we introduce a novel tool for analyzing and labeling the alveolar nerve from Cone Beam Computed Tomography (CBCT) 3D volumes
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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