The Rey-Osterrieth Complex Figure (ROCF) Test Assessment project uses deep learning models to extract useful information from this drawing-based neuropsychological test. This information could be leveraged by psychology professionals to improve the early diagnosis of cognitive impairment in adults.

As part of this project, we have produced two scientific articles:

Contact:

          
  • Mariano Rincón, Artificial Intelligence Department (UNED). Email: mrincon@dia.uned.es
  • Juan Guerrero Martín, Artificial Intelligence Department (UNED). Email: jguerrero@dia.uned.es
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Archivo ZIP - 2,9 MB - MD5: 29d8a4f8415e8cdc6226b99b3ccd4e23
Datos
Name: rocfd528_binary_images Content: 528 binary images of Rey-Osterrieth Complex Figures. Format: Portable Network Graphics Language: English Version: 1
Sep 27, 2024
Juan Guerrero Martín; Alba Gómez-Valadés Batanero; Estela Díaz López; Margarita Bachiller Mayoral; José Manuel Cuadra Troncoso; Rafael Martínez Tomás; Sara García Herranz; María del Carmen Díaz Mardomingo; Peraita, Herminia; Mariano Rincón Zamorano, 2024, "Subset of Quick, Draw! dataset for neural network pre-training / Subconjunto del conjunto de datos Quick, Draw! para pre-entrenamiento de redes neuronales", https://doi.org/10.21950/GWO9RA, e-cienciaDatos, V1
Description of the project This dataset is the result of the research carried out in the project "A Benchmark for Rey-Osterrieth Complex Figure (ROCF) Test Automatic Scoring", whose main goal was to establish a baseline for the scoring task consisting of: a dataset with 528 ROCF and results obtained by several deep learning models, as well as, by a...
Archivo ZIP - 1,8 GB - MD5: 6d485af0349e4c711194396d89bc5be7
Data
Name: qdsd414k . Content: 414000 drawings of Quick, Draw! dataset distributed in several folders representing each of the classes and the subdivisions of the dataset (training, validation and test). Format: Portable Network Graphics . Version: 1
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