1,721,011 research outputs found
Explainable deep learning for medical image processing: computer-aided diagnosis and robot-assisted surgery.
Full text linkThe recent advancements in the surging field of Deep Learning (DL) have revolutionized every sphere of life, and the healthcare domain is no exception. The enormous success of DL models, particularly with image data, has led to the development of several computeraided diagnosis and clinical support systems. These intelligent imaging systems can help physicians in numerous medical tasks including classification and staging of the various diseases, image-guided surgical procedures, and many more. Additionally, the proliferation of medical datasets has further facilitated the applications of DL techniques in healthcare realm. Moreover, all the perks DL offers are remarkable, however, DL architectures are typically blackbox, i.e. they hide the decision making mechanism, therefore, interpreting how the model arrived at a particular decision is hidden. Additionally, Convolutional Neural Networks (CNNs), which are most widely used DL techniques, are prone to adversarial examples, where small, imperceptible perturbations to the input data can cause the model to make incorrect predictions. These facts question the applicability of DL in healthcare sector where explainability holds paramount significance to build a trust on surging field of machine learning. The concept of eXplainable Artificial Intelligence (XAI) brings forward the possibility of explaining the results of DL models and reveals how the models produce results. These techniques aim to improve the transparency and interpretability of AI models, which can enhance trust in their results and facilitate their adoption in clinical practice. XAI approaches have the potential to advance the understanding of complex medical image analysis tasks and improve the reliability of AI-based diagnosis and treatment planning. The story does not end here, the XAI methods in the context of medical imaging generally produce saliency maps and compute feature importance to explain the results of DL models. The sensitive nature of healthcare industry, because of having the direct correlation with human life, questions the authenticity of XAI outcomes, and demands a qualitative and quantitative measure to evaluate these evaluation methods. Furthermore, heatmap visualizations alone are often insufficient for achieving transparency and interpretability of DL models in medical imaging to foster the AI and biomedical synergy. Inspired by the latest trends and contributions in light of the aforementioned concerns, this thesis designs, develops, and validates an interpretable and transparent intelligent clinical decision support system based on traditional machine and DL architectures, whose outcomes can be qualitatively and quantitatively explained with XAI methods. The thesis also comprises a segmentation and detection pipeline for image-driven surgical applications. These novel intelligent systems aims to assist the physicians and clinicians in image-guided diagnostic and treatment systems. The developed interpretable diagnostic frameworks offer wide range of applications and can be extended to several clinical scenarios. Concerning the XAI, transparency and interpretability of CNN architectures are achieved through two families of XAI methods, i.e. perceptive and mathematical XAI. Furthermore, within each of these XAI families, two explanation frameworks are employed. These methods facilitated to investigate the reliability of features and learning process, to critically analyse various CNN architectures and XAI methods, and to compare the outcomes of both XAI pipelines. To further highlight the applications of DL in the image-guided surgical domain, a case study has been performed on image-guided surgical procedures and interventions. The case study also encompasses a detailed investigative study of public datasets and presents the legal and ethical issues of DL-driven image-guided surgery. The study additionally underlines the risks and limitations towards the autonomous systems and provides the future perspective. Finally, the second case study investigates the qualitative and quantitative evaluation of the XAI techniques in regards to the medical images. The case study also sheds light on the evaluation measures, metrics for XAI, quality of explanation, types of explanation, and few more. The clinical efficacy of the developed solutions is evaluated through comparison with existing state-of-the-art methods, and is further validated through consultation with physicians where feasible. The datasets incorporated during the study are either obtained from the online open source platforms or collected from local health institutions
Innovative methodologies in agriculture for high-throughput plant phenomics using computer vision and artificial intelligence
Full text linkLa fenotipizzazione delle piante è essenziale per il miglioramento genetico e la gestione delle colture, ma i metodi tradizionali sono laboriosi e soggetti a errori. La visione artificiale e il deep learning (DL) offrono soluzioni rapide e accurate per analizzare le immagini delle piante. Tuttavia, la gestione dei dati, l’annotazione e la pre-elaborazione per i modelli di deep learning possono essere costose e richiedere molto tempo. Inoltre, i modelli avanzati potrebbero necessitare di modifiche architetturali per ridurre i costi computazionali, semplificare i modelli e migliorarne le prestazioni per una diagnostica ottimale.
Questo studio si propone di rivedere sistematicamente gli elementi chiave hardware e software che influenzano la fenotipizzazione ad alta capacità delle piante. Verranno analizzati in profondità i software e gli algoritmi utilizzati in questo campo. In particolare, la ricerca metterà in evidenza le metodologie innovative nella gestione dei dati e identificherà gli algoritmi più efficaci per analizzare i dati generati dalle piattaforme di fenotipizzazione delle piante utilizzando visione artificiale e intelligenza artificiale in un contesto di laboratorio.
Un modello di deep learning (YOLOv5) è stato progettato per riconoscere efficacemente diverse caratteristiche morfologiche in una vasta gamma di specie vegetali. Questo modello, combinato con il transfer learning e rigorose tecniche di valutazione, ha raggiunto punteggi particolarmente elevati in termini di precisione, richiamo (recall) e F1-measure, affrontando abilmente le sfide uniche poste dalle immagini di input.
Questa ricerca introduce un approccio innovativo per affrontare gli squilibri nei dataset mediante tecniche di bilanciamento dei dati. Aggregando i dati e generando campioni aggiuntivi per le classi sottorappresentate, il dataset viene riequilibrato. Inoltre, un modulo di attenzione è stato integrato nell’architettura del modello proposto (YOLOv8) per migliorare la capacità di rilevamento delle classi target. Questi metodi consentono l’addestramento di modelli di deep learning con una precisione significativamente migliorata. È stata presentata l’ottimizzazione delle modifiche alla testa del modello per migliorare il rilevamento di piccoli oggetti, utilizzando l’architettura di base di YOLOv8. Di conseguenza, il modello integrato SO-YOLOv5 dimostra una maggiore accuratezza nel rilevamento di piccoli oggetti, riducendo al contempo i costi computazionali e mantenendo la semplicità.
Un approccio alternativo al problema della segmentazione RSA è stato presentato, utilizzando la classificazione binaria mediante la stima di mappe probabilistiche per classificare i pixel delle immagini originali come sfondo o primo piano. Questo lavoro introduce una pipeline di elaborazione completa per l’analisi end-to-end degli RSA in ambienti industriali.
In conclusione, questa tesi sviluppa metodi per ridurre il tempo e lo sforzo, aumentando al contempo accuratezza e prestazioni nell’applicazione dei modelli DL alla fenotipizzazione delle piante. Esamina rilevatori a stadio singolo in grado di rilevare le parti aeree delle piante e una pipeline di elaborazione completa per l’analisi end-to-end degli RSA industriali tramite modelli CNN. Rendendo i modelli DL più accessibili e scalabili, questa ricerca avanza nel campo della fenotipizzazione delle piante e della produzione agricola.Plant phenotyping is essential for plant breeding and crop management, but traditional methods are labor-intensive and prone to errors. Computer vision and deep
learning (DL) offer solutions by rapidly and accurately analyzing plant images.
However, data management, annotation, and preprocessing for deep learning models
can be costly and time-consuming. Additionally, advanced models may require
architectural modifications to minimize computational costs, streamline the models,
and enhance their performance for optimal diagnostics.
This study seeks to systematically review the key hardware and software elements that influence high-throughput plant phenotyping. It will also delve deeply
into the software and algorithms used in this field. The research will particularly
emphasize innovative methodologies in data management and pinpoint the most
effective algorithms for analyzing data generated by plant phenotyping platforms
using computer vision and artificial intelligence within a laboratory setting. A deep
learning model (YOLOv5) be designed to effectively recognize diverse morphological features across various plant species. This model, coupled with transfer learning
and rigorous evaluation techniques, achieved notably high scores in precision, recall,
and F1-measure, adeptly addressing the unique challenges posed by the input images.
This research introduces an innovative approach to address dataset imbalances
using data balancing techniques. By pooling the data and generating extra samples for
underrepresented classes, the dataset is rebalanced. Moreover, an attention module
is integrated into the proposed head model architecture (YOLOv8) to enhance
the detection capability of target classes. These methods enable the training of
deep learning models with significantly improved accuracy. The optimization of
model head adaptations to enhance the detection of small objects was presented,
utilizing the basic architecture of YOLOv8. As a result, the integrated SO-YOLOv5
model demonstrates higher accuracy in detecting small objects while minimizingv
computational costs and maintaining simplicity. An alternative approach to the
RSA segmentation problem was presented, employing binary classification through
probabilistic map estimation to classify original image pixels as background or
foreground. This work introduces a comprehensive processing pipeline for end-toend analysis of factory RSAs.
In conclusion, this thesis develops methods to reduce time and effort and also
increase accuracy and performance for applying DL models in plant phenotyping.
It investigates single-stage detectors that can detect aerial parts of plants, and a
comprehensive processing pipeline for end-to-end analysis of factory RSAs by
CNNs models. By making DL models more accessible and scalable, this research
advances plant phenotyping and crop productio
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
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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