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Item type:Publication, A case report of sarcoidosis and ulcerative colitis: overlap or coexistence(Sociedad Espanola de Patologia Digestiva (SEPD), 2024); Gonzalo Carrasco-Avino4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Development of an artificial intelligence powered software for automated analysis of skeletal muscle ultrasonography(Springer Science and Business Media LLC, 2025-04-29); 1Scopus© Citations 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, White matter volume and microstructural integrity are associated with fatigue in relapsing multiple sclerosis(Springer Science and Business Media LLC, 2025-05-12); 10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, New Dermoscopy Pattern in Nevus‐Associated Melanomas(Wiley, 2025-04-19)2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Diagnostic imaging in Chile: a population-based description of its use in hospitalized patients(Publicidad Permanyer, SLU, 2024-09-10); 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Interpretable multimodal classification for age-related macular degeneration diagnosis(2024); Xu Yanwu<jats:p>Explainable Artificial Intelligence (XAI) is an emerging machine learning field that has been successful in medical image analysis. Interpretable approaches are able to “unbox” the black-box decisions made by AI systems, aiding medical doctors to justify their diagnostics better. In this paper, we analyze the performance of three different XAI strategies for medical image analysis in ophthalmology. We consider a multimodal deep learning model that combines optical coherence tomography (OCT) and infrared reflectance (IR) imaging for the diagnosis of age-related macular degeneration (AMD). The classification model is able to achieve an accuracy of 0.94, performing better than other unimodal alternatives. We analyze the XAI methods in terms of their ability to identify retinal damage and ease of interpretation, concluding that grad-CAM and guided grad-CAM can be combined to have both a coarse visual justification and a fine-grained analysis of the retinal layers. We provide important insights and recommendations for practitioners on how to design automated and explainable screening tests based on the combination of two image sources.</jats:p>Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Histiocitosis de Erdheim Chester como desafío diagnóstico ante un cuadro sistémico: Reporte de un caso(2024)Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Lateral prefrontal theta oscillations causally drive a computational mechanism underlying conflict expectation and adaptation(2024); ; Scopus© Citations 3 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds(2023)30Scopus© Citations 22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, “NASCI case of the month: left ventricular apical hypoplasia”(2023)10Scopus© Citations 1