1 a 5 de 5 Resultados
13 jun. 2024
Molina Moreno, Miguel; González Díaz, Iván; Rivera Gorrín, Maite; Burguera Vion, Víctor; Díaz de María, Fernando, 2024, "URI-CAD: Computer-Aided Diagnosis for Ultrasound Renal Imaging - IRYCIS (Hospital Ramón y Cajal)", https://doi.org/10.21950/0E0CTF, e-cienciaDatos, V1
The 2D ultrasound imaging database contains the kidney location and complete diagnosis of 1985 images (with 450 healthy and 1535 pathological kidneys), annotated by two experienced nephrologists from Hospital Universitario Ramón y Cajal. The annotations for each clinical cases in... |
20 feb. 2024 - BODYinTRANSIT (Sensory-driven Body Transformation Experiences On-the-move)
Tajadura‑Jiménez, Ana; Crucianelli, Laura; Zheng, Rebecca; Cheng, Chloe; Ley‑Flores, Judith; Borda‑Más, Mercedes; Bianchi‑Berthouze, Nadia; Fotopoulou, Aikaterini, 2024, "FootStep illusion_Eating disorders", https://doi.org/10.21950/VEEYMS, e-cienciaDatos, V1
The dataset consisted of measurements obtained in two Experiments from 73 female participants aged between 18 and 49 years old. In Experiment 1, we tested healthy women assigned to three groups based on self-reported Symptomatology of Eating Disorders as assessed by a questionnai... |
10 ene. 2024
Martínez-De Miguel, Claudia; Segura Bedmar, Isabel; Chacón-Solano, Esteban; Guerrero-Aspizua, Sara, 2024, "RareDis corpus", https://doi.org/10.21950/DEURZF, e-cienciaDatos, V1
Para poder aplicar técnicas de PLN y aprendizaje profundo capaces de reconocer de forma automática las menciones de las enfermedades raras y sus manifestaciones clínicas en textos es imprescindible contar con un dataset (colección de textos anotados con enfermedades y síntomas /... |
9 ene. 2024
Segura Bedmar, Isabel; Camino Perdones, David; Guerrero Aspizua, Sara, 2024, "NLP4RARE-NER", https://doi.org/10.21950/S2IRKE, e-cienciaDatos, V1
El dataset contiene el código de la implementación y experimentación de los enfoques propuestos en el artículo: Isabel Segura Bedmar, David Camino Perdones, Sara Guerrero Aspizua. (2022). Exploring deep learning methods for recognizing rare diseases and their clinical manifestati... |
8 ene. 2024
Muñoz Organero, Mario, 2024, "Dataset for sample T1DM simulated patients used to validate the paper Muñoz-Organero, M. “Deep physiological model for blood glucose prediction in T1DM patients”. Sensors (Switzerland), 2020, 20(14), pp. 1–17, 3896", https://doi.org/10.21950/MMVBUJ, e-cienciaDatos, V1
The AIDA diabetes simulator has been used to generate 10 days of data for the different models implemented by the tool. |
