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Araya, Mauricio
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Nombre
Araya, Mauricio
Departamento
Campus / Sede
Campus Casa Central Valparaiso
Email
ORCID
Scopus Author ID
36442186000
Now showing 1 - 2 of 2
- PublicationA Data Ingestion Procedure towards a Medical Images Repository(2024-08-01)
; ;Castañeda, Victor; ; This article presents an ingestion procedure towards an interoperable repository called ALPACS (Anonymized Local Picture Archiving and Communication System). ALPACS provides services to clinical and hospital users, who can access the repository data through an Artificial Intelligence (AI) application called PROXIMITY. This article shows the automated procedure for data ingestion from the medical imaging provider to the ALPACS repository. The data ingestion procedure was successfully applied by the data provider (Hospital Clínico de la Universidad de Chile, HCUCH) using a pseudo-anonymization algorithm at the source, thereby ensuring that the privacy of patients’ sensitive data is respected. Data transfer was carried out using international communication standards for health systems, which allows for replication of the procedure by other institutions that provide medical images. Objectives: This article aims to create a repository of 33,000 medical CT images and 33,000 diagnostic reports with international standards (HL7 HAPI FHIR, DICOM, SNOMED). This goal requires devising a data ingestion procedure that can be replicated by other provider institutions, guaranteeing data privacy by implementing a pseudo-anonymization algorithm at the source, and generating labels from annotations via NLP. Methodology: Our approach involves hybrid on-premise/cloud deployment of PACS and FHIR services, including transfer services for anonymized data to populate the repository through a structured ingestion procedure. We used NLP over the diagnostic reports to generate annotations, which were then used to train ML algorithms for content-based similar exam recovery. Outcomes: We successfully implemented ALPACS and PROXIMITY 2.0, ingesting almost 19,000 thorax CT exams to date along with their corresponding reports. - PublicationHDMClouds: A hierarchical decomposition of molecular clouds based on Gaussian mixtures(2019-01-21)
;Villanueva, Martín; ;Torres, Claudio E.Amigo, PíaThe identification and characterization of independent entities within molecular clouds is a key challenge for astronomical data analysis. The ever-increasing volume, resolution and sensitivity of observations demand automatic routines to identify and deblend candidate entities to be analysed. Additionally, the intrinsically hierarchical nature of molecular gas distributions demands an automatic identification of the nesting relations between these entities. We propose a novel approach for decomposing molecular clouds in two steps: first we fit the data to a Gaussian mixture with many components, then reconstruct the cloud using a hierarchical model using a Gaussian-mixture reduction algorithm. We use a continuous-space representation, because it is well suited for disentangling coupled entities of emission compared with pixel-based ones, and build a tree structure to represent the hierarchical connections between mixture components. This allows us to select different groups of components in the tree without additional computational effort, including overlapping substructures. We assess our proposal quantitatively and qualitatively using data from the Atacama Large Millimeter Array (ALMA) science verification archive, as well as synthetic data. We also compare the results from some state-of-the-art clump identification algorithms. The experiments and comparisons show that our approach is an effective way to inspect and represent the hierarchical structure of molecular clouds.