1,721,118 research outputs found
Grounding concepts as emerging clusters in multiple conceptual spaces
No full textA novel framework for symbol grounding in artificial agents is presented, which relies on the key idea that concepts "emerge" implicitly at the perceptual level as clusters of points with similar features forming homogeneous regions in multiple perceptual Conceptual Spaces (pCS). Such spaces describe percepts such as color, texture, shape, and position that in turn are the properties of the objects populating the agent's environment. Objects are represented in a suitable object Conceptual Space where all their features are composed together again using clustering in pCSs. Symbols will be learned from such a tensor space. A detailed description of both the framework and its theoretical foundations are reported and discussed in this work
DR-Minerva: A Multimodal Language Model Based on Minerva for Diagnostic Information Retrieval
No full textThis paper illustrates the development of Minerva Diagnostic Retriever (DR-Minerva), a Visual Language Model specialized in the medical domain. Prompted using a textual input with the patient’s information along with a CT or MR scan, the model provides information about the body part and the scanning modality of the given image. The model relies on the Flamingo architecture, which is well known for its good in-context and few-shot learning capabilities, and it encodes textual data using Minerva, a novel Large Language Model trained on English and Italian data. Model performances are improved via fine-tuning the aforementioned model, and using external knowledge by means of a Retrieval Augmented Generation approach. At inference time, the model is injected with the retrieved examples in form of in-context learning. The authors developed a rearranged version of the MedPix® multi-modal medical dataset, that was used for both the development and the test of the model as long as for retrieval. A detailed description of the system is reported along with the experimental results that are discussed in thoroughly. Dataset and models used are available on GitHub (https://github.com/CHILab1/MedPix-2.0.)
A framework for data-driven adaptive GUI generation based on DICOM
No full textComputer applications for diagnostic medical imaging provide generally a wide range of tools to support physicians in their daily diagnosis activities. Unfortunately, some functionalities are specialized for specific diseases or imaging modalities, while other ones are useless for the images under investigation. Nevertheless, the corresponding Graphical User Interface (GUI) widgets are still present on the screen reducing the image visualization area. As a consequence, the physician may be affected by cognitive overload and visual stress causing a degradation of performances, mainly due to unuseful widgets. In clinical environments, a GUI must represent a sequence of steps for image investigation following a well-defined workflow. This paper proposes a software framework aimed at addressing the issues outlined before. Specifically, we designed a DICOM based mechanism of data-driven GUI generation, referring to the examined body part and imaging modality as well as to the medical image analysis task to perform. In this way, the self-configuring GUI is generated on-the-fly, so that just specific functionalities are active according to the current clinical scenario. Such a solution provides also a tight integration with the DICOM standard, which considers various aspects of the technology in medicine but does not address GUI specification issues. The proposed workflow is designed for diagnostic workstations with a local file system on an interchange media acting inside or outside the hospital ward. Accordingly, the DICOMDIR conceptual data model, defined by a hierarchical structure, is exploited and extended to include the GUI information thanks to a new Information Object Module (IOM), which reuses the DICOM information model. The proposed framework exploits the DICOM standard representing an enabling technology for an auto-consistent solution in medical diagnostic applications. In this paper we present a detailed description of the framework, its software design, and a proof-of-concept implementation as a suitable plug-in of the OsiriX imaging software
Named entity recognition and linking in tweets based on linguistic similarity
No full textThis work proposes a novel approach in Named Entity rEcognition and Linking (NEEL) in tweets, applying the same strategy already presented for Question Answering (QA) by the same authors. The previous work describes a rule-based and ontology-based system that attempts to retrieve the correct answer to a query from the DBPedia ontology through a similarity measure between the query and the ontology labels. In this paper, a tweet is interpreted as a query for the QA system: both the text and the thread of a tweet are a sequence of statements that have been linked to the ontology. Provided that tweets make extensive use of informal language, the similarity measure and the underlying processes have been devised differently than in the previous approach; also the particular structure of a tweet, that is the presence of mentions, hashtags, and partially structured statements, is taken into consideration for linguistic insights. NEEL is achieved actually as the output of annotating a tweet with the names of the ontological entities retrieved by the system. The strategy is explained in detail along with the architecture and the implementation of the system; also the performance as compared to the systems presented at the #Micropost2016 workshop NEEL Challenge co-located with the World Wide Web conference 2016 (WWW â 16) is reported and discussed
Modeling ontologies for robotic environments
No full textOn the basis of a multiple abstraction levels specification process, we developed a representational model for environmental robotic knowledge through the definition of a set of ontologies using a multi perspective approach. A general ontological model for typical indoor environments has been first developed, followed by its specialization using an implementation perspective. Actual software implementation of the ontology has been obtained via a XML-based markup language, used to build a repository for robotic environmental knowledge. Copyright 2002 ACM
A PCA Interpretation of the Glasgow Coma Scale in the Trauma Brain Injury PECARN Dataset
No full textCT scan is strongly recommended for a patient affected by head trauma, but he/she must absorb a certain amount of radiations. For this reason, the physician tries to avoid such a practice for pediatric patients. The symptoms analysis, visual/tactile inspection, and reactions to appropriate stimuli from the physician could induce him/her to put the patient in a period of observation instead of performing an immediate CT scan. As a consequence, the correct evaluation of those symptoms is a crucial task. For this reason, the Pediatric Glasgow Coma Scale (PGCS) plays a fundamental role, because it is a numeric scale regarding the patient's mental status. It is computed as the sum of the score for the eye, motor and verbal response evaluated by the physician. In this paper, the Principal Component Analysis (PCA) is performed on the PGCS of the Trauma Brain Injury (TBI) dataset collected by the PECARN (Pediatric Emergency Care Applied Research Network). The PCA is performed in all cases when the sum of the three partial scores results in a value less than 14, because a patient with PGCS = 15 is not considered at risk. Under this constraint, the PCA reveals that each partial GCS give the same contribution to the first principal component, but a scale variation is introduced
DicomOS: A Preliminary Study on a Linux-Based Operating System Tailored for Medical Imaging and Enhanced Interoperability in Radiology Workflows
Full text linkIn this paper, we propose a Linux-based operating system, namely, DicomOS, tailored for medical imaging and enhanced interoperability, addressing user-friendly functionality and the main critical needs in radiology workflows. Traditional operating systems in clinical settings face limitations, such as fragmented software ecosystems and platform-specific restrictions, which disrupt collaborative workflows and hinder diagnostic efficiency.
Built on Ubuntu 22.04 LTS, DicomOS integrates essential DICOM functionalities directly into the OS, providing a unified, cohesive platform for image visualization, annotation, and sharing. Methods include custom configurations and the development of graphical user interfaces (GUIs) and command-line tools, making them accessible to medical professionals and developers. Key applications such as ITK-SNAP and 3D Slicer are seamlessly integrated alongside specialized GUIs that enhance usability without requiring extensive technical expertise. As preliminary work, DicomOS demonstrates the potential to simplify medical imaging workflows, reduce cognitive load, and promote efficient data sharing across diverse clinical settings. However, further evaluations, including structured clinical tests and broader deployment with a distributable ISO image, must validate its effectiveness and scalability in real-world scenarios. The results indicate that DicomOS provides a versatile and adaptable solution, supporting radiologists in routine tasks while facilitating customization for advanced users. As an open-source platform, DicomOS has the potential to evolve alongside medical imaging needs, positioning it as a valuable resource for enhancing workflow integration and clinical collaboration
Biologically Inspired Cognitive Architectures 2012
No full textThe challenge of creating a real-life computational equivalent of the human mind requires that we better understand at a computational level how natural intelligent systems develop their cognitive and learning functions. In recent years, biologically inspired cognitive architectures have emerged as a powerful new approach toward gaining this kind of understanding (here “biologically inspired” is understood broadly as “brain-mind inspired”). Still, despite impressive successes and growing interest in BICA, wide gaps separate different approaches from each other and from solutions found in biology. Modern scientific societies pursue related yet separate goals, while the mission of the BICA Society consists in the integration of many efforts in addressing the above challenge. Therefore, the BICA Society shall bring together researchers from disjointed fields and communities who devote their efforts to solving the same challenge, despite that they may “speak different languages”. This will be achieved by promoting and facilitating the transdisciplinary study of cognitive architectures, and in the long-term perspective – creating one unifying widespread framework for the human-level cognitive architectures and their implementations. This book is a proceedings of the Third Annual Meeting of the BICA Society, which was hold in Palermo-Italy from October 31 to November 2, 2012. The book describes recent advances and new challenges around the theme of understanding how to create general-purpose humanlike artificial intelligence using inspirations from studies of the brain and the mind
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
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