1,720,972 research outputs found
Development and discussion of deep learning algorithms for breast density classification and for COVID-19 lesions quantification on CT scans : towards a real-time multidisciplinary approach
Full text linkDeep Learning and Medical Image Analysis: Epistemology and Ethical Issues
No full textMachine and deep learning methods applied to medicine seem to be a promising way to improve the perfor-mance in solving many issues from the diagnosis of a disease to the prediction of personalized therapies byanalyzing many and diverse types of data. However, developing an algorithm with the aim of applying it inclinical practice is a complex task which should take into account the context in which the software is devel-oped and should be used. In the first report of the World Health Organization (WHO) about the ethics andgovernance of Artificial Intelligence (AI) for health published in 2021, it has been stated that AI may improvehealthcare and medicine all over the world only if ethics and human rights are a main part of its development.Involving ethics in technology development means to take into account several issues that should be discussedalso inside the scientific community: the epistemological changes, population stratification issues, the opacityof deep learning algorithms, data complexity and accessibility, health processes and so on. In this work, someof the mentioned issues will be discussed in order to open a discussion on whether and how it is possible to address them
Increasing toxicity of enrofloxacin over four generations of Daphnia magna
Full text linkThe effects of both continuous and alternate exposure to 2 mg L-1 of enrofloxacin (EFX) on survival, growth and reproduction were evaluated over four generations of Daphnia magna. Mortality increased, reaching 100% in most groups by the end of the third generation. Growth inhibition was detected in only one group of the fourth generation. Reproduction inhibition was > 50% in all groups and, in second and third generations, groups transferred to pure medium showed a greater inhibition of reproduction than those exposed to EFX. To verify whether the effects observed in these groups could be explained by the perinatal exposure to the antibacterial, a reproduction test with daphnids obtained from in vitro exposed D. magna embryos was also carried out. Perinatal exposure to EFX seemed to act as an ‘all-or-nothing’ toxicity effect as 31.4% of embryos died, but the surviving daphnids did not show any inhibition of reproduction activity. However, the embryonic mortality may at least partially justify the inhibition of reproduction observed in exposed groups along the multigenerational test. Concluding, the multigenerational test with D. magna did show disruption to a population that cannot be evidenced by the official tests. The increasing deterioration across generations might be inferred as the consequence of heritable alterations. Whilst the concentration tested was higher than those usually detected in the natural environment, the increasing toxicity of EFX across generations and the possible additive toxicity of fluoroquinolone mixtures, prevent harm to crustacean populations by effects in the real context from being completely ruled out
EXPLAINING THE BEHAVIOUR OF A CONVOLUTIONAL NEURAL NETWORK FOR BREAST DENSITY ASSESSMENT
No full textExplainability Applied to a Deep-Learning Based Algorithm for Lung Nodule Segmentation
No full textDeep learning and computer-aided detection (CAD) methods play a pivotal role in the early detection and diagnosis of various cancer types. The significance of AI in the medical field has become particularly pronounced during the coronavirus pandemic. This study aims to develop a deep learning-based system for segmenting and detecting nodules in the lung parenchyma, utilizing the Luna-16 challenge dataset. The algorithm is divided into two phases: the first phase involves lung segmentation using the previously developed LungQuant algorithm to identify the region of interest (ROI), and the second phase employs a specifically designed and fine-tuned Attention Res-UNet for nodule segmentation. Additionally, the explainable AI (XAI) technique, Grad-CAM, was used to demonstrate the reliability of the proposed algorithm for clinical application. In the initial phase, the LungQuant algorithm achieved an average Dice Similarity Coefficient (DSC) of 90%. For nodule segmentation, the DSC scores were 81% test sets. The model also achieved average sensitivity and specificity metrics of 0.86 and 0.92
Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders
Full text linkBackground:
The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD).
Material and methods
We analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections. We considered 1383 male subjects with age between 5 and 40 years, including 680 subjects with ASD and 703 TD from 35 different acquisition sites. We extracted morphometric and functional brain features from MRI scans with the Freesurfer and the CPAC analysis packages, respectively. Then, due to the multisite nature of the dataset, we implemented a data harmonization protocol. The ASD vs. TD classification was carried out with a multiple-input DL model, consisting in a neural network which generates a fixed-length feature representation of the data of each modality (FR-NN), and a Dense Neural Network for classification (C-NN). Specifically, we implemented a joint fusion approach to multiple source data integration. The main advantage of the latter is that the loss is propagated back to the FR-NN during the training, thus creating informative feature representations for each data modality. Then, a C-NN, with a number of layers and neurons per layer to be optimized during the model training, performs the ASD-TD discrimination. The performance was evaluated by computing the Area under the Receiver Operating Characteristic curve within a nested 10-fold cross-validation. The brain features that drive the DL classification were identified by the SHAP explainability framework.
Results
The AUC values of 0.66±0.05 and of 0.76±0.04 were obtained in the ASD vs. TD discrimination when only structural or functional features are considered, respectively. The joint fusion approach led to an AUC of 0.78±0.04. The set of structural and functional connectivity features identified as the most important for the two-class discrimination supports the idea that brain changes tend to occur in individuals with ASD in regions belonging to the Default Mode Network and to the Social Brain.
Conclusions
Our results demonstrate that the multimodal joint fusion approach outperforms the classification results obtained with data acquired by a single MRI modality as it efficiently exploits the complementarity of structural and functional brain information
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
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