1,721,048 research outputs found
Using a Multiple Experts System to Classify Fluorescence Intensity in Antinuclear Autoantibodies Analysis
No full textPyTrack: A Map-Matching-Based Python Toolbox for Vehicle Trajectory Reconstruction
No full textThe exponential growth of IoT devices, smartphones, smartwatches, and vehicles equipped with positioning technology, such as Global Positioning System (GPS) modules, has boosted the development of location-based services for several applications in Intelligent Transportation Systems. However, the inherent error of location-based technologies makes it necessary to align the positioning trajectories to the actual underlying road network, a process known as map-matching. To the best of our knowledge, there are no comprehensive tools that allow us to model street networks, conduct topological and spatial analyses of the underlying street graph, perform map-matching processes on GPS point trajectories, and deeply analyse and elaborate these reconstructed trajectories. To address this issue, we present PyTrack, an open-source map-matching-based Python toolbox designed for academics, researchers and practitioners that integrate the recorded GPS coordinates with data provided by the OpenStreetMap, an open-source geographic information system. This manuscript overviews the architecture of the library offering a detailed description of its capabilities and modules. Besides, we provide an introductory guide to getting started with PyTrack covering the most fundamental steps of our framework. For more information on PyTrack, users are encouraged to visit the official repository at https://github.com/cosbidev/PyTrack or the official documentation at https://pytrack-lib.readthedocs.io
Multi-stage intermediate fusion for multimodal learning to classify non-small cell lung cancer subtypes from CT and PET
No full textAccurate classification of histological subtypes of non-small cell lung cancer (NSCLC) is essential in the era of precision medicine, yet current invasive techniques are not always feasible and may lead to clinical complications. This study presents MINT, a Multi-stage INTermediate fusion approach to classify NSCLC subtypes from CT and PET images. Our method integrates the two modalities at different stages of feature extraction, using voxel-wise fusion to exploit complementary information across varying abstraction levels while preserving spatial correlations. We compare our method against unimodal approaches using only CT or PET images to demonstrate the benefits of modality fusion, and further benchmark it against early and late fusion techniques to highlight the advantages of intermediate fusion during feature extraction. Additionally, we compare our model with the only existing intermediate fusion method for histological subtype classification using PET/CT images. Our results demonstrate that the proposed method outperforms all alternatives across key metrics, with an accuracy and AUC equal to 0.724 and 0.681, respectively. This non-invasive approach has the potential to significantly improve diagnostic accuracy, facilitate more informed treatment decisions, and advance personalized care in lung cancer management
Exploring Early Stress Detection from Multimodal Time Series with Deep Reinforcement Learning
No full textIn our fast-paced world, timely access to information is essential. This urgency is highlighted in stress detection, where swift actions can mitigate harmful psycho-physiological effects. We introduce an early stress detection method using Deep Reinforcement Learning (DRL). This method utilizes DRL to efficiently analyze time series data segments, aiming for accurate and quick stress classification. We employ a dynamic observation window strategy, allowing the DRL agent to adjust based on data complexity. Our evaluations, performed on a public dataset using a Leave-One-Subject-Out (LOSO) method, emphasize DRL's potential in stress detection. The related code is available at https://github.com/cosbidev/DRL-4-Early-Stress-Detection
On the use of classification reliability for improving performance of the one-per-class decomposition method
No full textTypical pattern recognition applications require to handle both binary and multiclass classification problems. Several researchers have pointed out that obtaining a classifier that discriminates between two classes is much easier than building one that simultaneously distinguishes among all classes. This observation has motivated substantial research on using a pool of binary classifiers to address multiclass problems. Such an approach is also named as decomposition method. Anyway, the performance of a given classification system can be sometimes unsatisfactory for the needs of real applications, especially when these are characterized by large data variability and/or significant amount of noise. In these cases it is important that the classification system is able to estimate the reliability of its decision for each sample under test. This estimate could be used, for example, for deciding to reject a sample instead of running the risk of misclassifying it, so improving the overall system performance. Based on these motivations, this paper defines a reliability estimator for decomposition schemes belonging to the One-per-Class framework. The estimator is based on the reliabilities provided by each binary classifier, on the status of their outputs while it is independent of their design. The performance of the proposed approach has been assessed on private and public medical datasets, showing that it can be used to improve the classification performance of the One-per-Class scheme with respect to both multiclass classifiers and other well-known decomposition schemes
Enhancing NSCLC Histological Subtype Classification: A Federated Learning Approach Using Triplet Loss
No full textLung cancer remains one of the leading causes of cancer-related deaths worldwide, with Non-Small Cell Lung Cancer (NSCLC) accounting for approximately 85% of all cases. Accurate histological subtype classification of NSCLC is crucial for personalized treatment planning and improving patient outcomes. Developing robust classification models for NSCLC subtypes often requires large, diverse datasets, which can be challenging to obtain due to privacy concerns and data silos. This study proposes an approach combining federated learning with triplet loss to address these challenges. We evaluated our method’s performance in classifying NSCLC subtypes using data from multiple institutions while preserving privacy. Our experiments compared the proposed federated learning approach with triplet loss against alternative methods, including local training and softmax loss. Results demonstrated that our federated learning approach with triplet loss consistently outperformed other methods across key metrics. The combination of federated learning and triplet loss showed synergistic effects, leveraging external datasets to improve model performance while maintaining data confidentiality. The source code for the implementation described in this paper is available at https://github.com/aksufatih/federated-triplet-histology
Time-window SIQR analysis of COVID-19 outbreak and containment measures in italy
No full textThe COVID-19 disease caused by the coronavirus SARS-nCoV2 is currently a global public health threat and Italy is one of the countries mostly suffering from this epidemic. It is therefore important to analyze epidemic data, considering also that the government deployed laws limiting the societal activities. We model COVID-19 dynamics with a SIQR (susceptible - infectious - quarantined - recovered) model, where we take into account the temporal variability of its parameters. Particle Swarm Optimization is used to find out the best parameters in the case of Italy and of Italian regions where the epidemic has the greatest impact. The basic reproductive number is estimated by a novel approach that averages out different PSO fits computed considering different temporal time-windows and reducing possible noise in the data. The results on data collected from February 24 to April 24 show that our approach is able to fit the data with low errors and that the basic reproductive number is characterized by a descending trend in time from 3.5 to a value below 1
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
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