Search alternatives:
process optimization » model optimization (Expand Search)
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
mask process » based process (Expand Search), basic process (Expand Search), a process (Expand Search)
binary mask » binary image (Expand Search)
process optimization » model optimization (Expand Search)
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
mask process » based process (Expand Search), basic process (Expand Search), a process (Expand Search)
binary mask » binary image (Expand Search)
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A* Path-Finding Algorithm to Determine Cell Connections
Published 2025“…</p><p dir="ltr">Astrocytes were dissociated from E18 mouse cortical tissue, and image data were processed using a Cellpose 2.0 model to mask nuclei. …”
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Image processing workflow.
Published 2020“…<p>Raw fluorescent microscope images (a) were processed with a binary segmentation algorithm, and clusters of bacterial cells were manually annotated. …”
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List of data tables.
Published 2025“…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …”
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Flow chart of data source inclusion.
Published 2025“…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …”
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Predictive model-building process.
Published 2025“…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …”
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Comparison of models performance metrics.
Published 2025“…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …”
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Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish)
Published 2024“…</li></ul><p dir="ltr"><b>File Structure</b></p><p dir="ltr">The code generates and saves:</p><ul><li>Weights of the trained model (.h5)</li><li>Configured tokenizer</li><li>Training history in CSV</li><li>Requirements file</li></ul><p dir="ltr"><b>Important Notes</b></p><ul><li>The model excludes category 2 during training</li><li>Implements transfer learning from a pre-trained model for binary hate detection</li><li>Includes early stopping callbacks to prevent overfitting</li><li>Uses class weighting to handle category imbalances</li></ul><p dir="ltr">The process of creating this algorithm is explained in the technical report located at: Blanco-Valencia, X., De Gregorio-Vicente, O., Ruiz Iniesta, A., & Said-Hung, E. (2025). …”
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Image 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.jpeg
Published 2025“…Predictors included demographics, comorbidities, laboratory parameters, vital signs, and disease severity scores. Missing data (<30%) were imputed using random forest. The cohort was split into training (75%) and internal testing (25%) sets, with hyperparameter optimization via 5-fold cross-validation. …”
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Table 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.docx
Published 2025“…Predictors included demographics, comorbidities, laboratory parameters, vital signs, and disease severity scores. Missing data (<30%) were imputed using random forest. The cohort was split into training (75%) and internal testing (25%) sets, with hyperparameter optimization via 5-fold cross-validation. …”
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PathOlOgics_RBCs Python Scripts.zip
Published 2023“…This process generated a ground-truth binary semantic segmentation mask and determined the bounding box coordinates (XYWH) for each cell. …”
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Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants
Published 2019“…Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-driven techniques based on machine learning (ML) might improve the performance of risk predictions by agnostically discovering novel risk predictors and learning the complex interactions between them. …”
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