Image-Analysis-Hub/Pasteur-BioImage-Analysis-Course-2025
May 2025 – May 2025
1 Month active
Languages Used
XML
Technical Skills
AI Pipeline DevelopmentData EngineeringData PreparationData ProcessingDeep LearningImage Analysis
carlosuc3m
PROFILE
Carlosuc3m
During May 2025, this developer delivered an end-to-end AI image analysis pipeline for the Image-Analysis-Hub/Pasteur-BioImage-Analysis-Course-2025 repository, focusing on deep learning and data engineering. They designed a workflow that processes raw image data, applies neural network models, performs thresholding and region-of-interest analysis, and exports results to Excel for downstream use. Their work included curating a comprehensive Day 2 training dataset with masks, objects, and CSV-backed statistics to support reproducible experiments and faster model iteration. Using XML and data processing techniques, they also improved project maintainability by reorganizing the codebase and standardizing folder naming conventions across the repository.
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
4Total
Bugs
2
Commits
4
Features
1
Lines of code
1,528
Activity Months1
Your Network
15 peopleSame Organization
@alumnos.uc3m.es1
Work History
May 2025
4 Commits • 1 Features
May 1, 2025May 2025: Delivered an end-to-end AI image analysis pipeline and Day 2 training dataset for the Pasteur BioImage course, establishing a repeatable workflow from data to analyzed results and Excel-ready reports. Implemented data curation with CSV-backed statistics for masks, objects, and sources, enabling faster model iteration and clearer performance insights. Completed foundational codebase cleanup and maintainability improvements, including folder naming consistency and a reorganization into ai_pipeline to reduce onboarding time and technical debt.
4 Commits • 1 Features
May 1, 2025May 2025: Delivered an end-to-end AI image analysis pipeline and Day 2 training dataset for the Pasteur BioImage course, establishing a repeatable workflow from data to analyzed results and Excel-ready reports. Implemented data curation with CSV-backed statistics for masks, objects, and sources, enabling faster model iteration and clearer performance insights. Completed foundational codebase cleanup and maintainability improvements, including folder naming consistency and a reorganization into ai_pipeline to reduce onboarding time and technical debt.
May 2025
Activity
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Quality Metrics
Correctness95.0%
Maintainability95.0%
Architecture95.0%
Performance90.0%
AI Usage35.0%
Skills & Technologies
Programming Languages
XML
Technical Skills
AI Pipeline DevelopmentData EngineeringData PreparationData ProcessingDeep LearningImage AnalysisMachine Learning
Repositories Contributed To
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