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
Developed an end-to-end AI image analysis pipeline for the Image-Analysis-Hub/Pasteur-BioImage-Analysis-Course-2025 repository, delivering a repeatable workflow from raw data to Excel-ready reports. The work included building deep learning-based image processing, region of interest analysis, and automated data curation with CSV-backed statistics for masks, objects, and sources. Leveraging skills in AI pipeline development, data engineering, and machine learning, the developer improved maintainability by reorganizing the codebase into a clear ai_pipeline structure and standardizing folder naming conventions. XML was used for data handling, supporting reproducible experiments and faster model iteration while reducing onboarding time and technical debt.
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
17 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
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