datasci4citizens/server-wounds
May 2025 – Jun 2025
2 Months active
Languages Used
BatchCSVGit IgnoreMarkdownPowerShellPythonTclShell
Technical Skills
Build ProcessBuild SystemsCSV HandlingCSV ProcessingCode CleanupComputer Vision
JoaoCeleste
PROFILE
Joaoceleste
Worked on the datasci4citizens/server-wounds repository to deliver an end-to-end wound assessment pipeline, focusing on data readiness, model development, and deployment reliability. Built features for dataset ingestion, data cleaning, and clustering, while developing supervised and unsupervised models for wound classification and infection detection. Enhanced the labeling workflow with a manual annotation tool and improved analytics by exporting predictions for downstream analysis. Integrated new datasets and expanded image coverage, ensuring balanced training data. Leveraged Python, PyTorch, and Pandas to implement robust backend and machine learning workflows, addressing both model performance and maintainability through code cleanup, documentation, and workflow automation.
Overall Statistics
Feature vs Bugs
82%Features
Repository Contributions
72Total
Bugs
6
Commits
72
Features
27
Lines of code
1,619,098
Activity Months2
Your Network
22 peopleSame Organization
@dac.unicamp.br10
Shared Repositories
12
Caio Maia Moreira SantosMember
caiomaia83Member
Andreas CisiMember
GoliasVictorMember
g169366Member
lucas-vrmMember
Miguel BuzatoMember
GuilhermeMember
Roger HonoratoMember
Work History
June 2025
32 Commits • 10 Features
Jun 1, 2025June 2025 monthly summary for datasci4citizens/server-wounds: Delivered end-to-end improvements to the wound assessment pipeline focusing on ML model enhancements, data ingestion, and deployment reliability. Key features include a new limited-classes classification model, an enhanced image ingestion and balance workflow, and a dedicated infection/ischemia detection model. Server integration improvements ensured robust label formatting and predict_single_image readiness, while prediction workflow enhancements streamlined end-to-end processing after image receipt and tightened dependencies. The work improves prediction speed, reliability, and maintainability, enabling faster clinical decision support and scalable deployment.
32 Commits • 10 Features
Jun 1, 2025June 2025 monthly summary for datasci4citizens/server-wounds: Delivered end-to-end improvements to the wound assessment pipeline focusing on ML model enhancements, data ingestion, and deployment reliability. Key features include a new limited-classes classification model, an enhanced image ingestion and balance workflow, and a dedicated infection/ischemia detection model. Server integration improvements ensured robust label formatting and predict_single_image readiness, while prediction workflow enhancements streamlined end-to-end processing after image receipt and tightened dependencies. The work improves prediction speed, reliability, and maintainability, enabling faster clinical decision support and scalable deployment.
June 2025
May 2025
40 Commits • 17 Features
May 1, 2025May 2025 (datasci4citizens/server-wounds) summary focusing on end-to-end data readiness, feature expansions, and labeling tooling that collectively improve data quality, model readiness, and analytics capabilities. The work delivered strengthens the data pipeline, expands dataset coverage, enhances labeling workflows, and improves repository hygiene, with clear business value in faster data preparation, better model training data, and reusable project artifacts for stakeholder discussions.
May 2025
40 Commits • 17 Features
May 1, 2025May 2025 (datasci4citizens/server-wounds) summary focusing on end-to-end data readiness, feature expansions, and labeling tooling that collectively improve data quality, model readiness, and analytics capabilities. The work delivered strengthens the data pipeline, expands dataset coverage, enhances labeling workflows, and improves repository hygiene, with clear business value in faster data preparation, better model training data, and reusable project artifacts for stakeholder discussions.
Activity
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Quality Metrics
Correctness86.0%
Maintainability86.8%
Architecture84.4%
Performance80.8%
AI Usage20.2%
Skills & Technologies
Programming Languages
BatchCSVGit IgnoreMarkdownPowerShellPythonShellTcl
Technical Skills
API DevelopmentBackend DevelopmentBuild ProcessBuild SystemsCSV HandlingCSV ProcessingCode CleanupComputer VisionData AnalysisData AugmentationData CleaningData ClusteringData EngineeringData ManagementData Parsing
Repositories Contributed To
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