MalariaAI is an advanced deep learning system designed for automated analysis and classification of medical images. This project demonstrates the ability to manage complex machine learning pipelines, from data preparation and model development to results visualization in a medical diagnostic support context using artificial intelligence.
This project was developed as a portfolio demonstration of the skills acquired in computer vision, data engineering, and production-ready machine learning systems.
Note: This project is the result of collaborative work where all team members contributed equally to this AWS learning opportunity. Thanks to all team members for the collaborative spirit, knowledge sharing, and collective effort that made this project successful:
- 🔬 @Vicio - Vicio Di Cara
- 🔬 @silvia - Silvia Garavaglia
- 🔬 @ania - Ania Jaca
- 🔬 @salome - Salome Bennet
- 🔬 @ferdous - Ferdous Miah
The source code repository is currently private and under active development. This README serves as a portfolio showcase of the project architecture, methodologies, and skills demonstrated. For inquiries about code access or collaboration opportunities, please reach out via the contact section below.
- Instance Segmentation: Implementation and optimization of pixel-level segmentation models
- Transfer Learning: Adaptation of pre-trained architectures to novel application domains
- Multi-Dataset Pipeline: Management of heterogeneous datasets with different annotation formats
- Model Evaluation: Advanced performance metrics (mAP, precision, recall, IoU)
- Data Standardization: Conversion between heterogeneous formats (JSON, XML, PNG masks, TXT coordinates)
- ETL Pipeline: Development of robust preprocessing and data normalization pipelines
- Quality Assurance: Dataset debugging and validation, handling of corrupted data
- Data Visualization: Exploratory analysis and visual analytics
- Training Pipeline: Implementation of incremental training workflows and checkpoint management
- Logging & Monitoring: Comprehensive system logging and metrics tracking
- Cloud Deployment: Optimization for Google Colab and AWS environments
- Resource Management: Memory optimization and GPU memory management
| Category | Technologies |
|---|---|
| Deep Learning Framework | PyTorch, state-of-the-art architectures |
| Computer Vision | Modern segmentation models, image processing |
| Data Processing | NumPy, Pandas, OpenCV |
| Visualization | Matplotlib, advanced analytics |
| Cloud Platforms | Google Colab, AWS infrastructure |
| Tools & Utilities | Python 3.8+, Jupyter Notebooks, version control |
The project is structured in a modular and scalable workflow:
notebooks/
├── 1. Analysis & Debugging # Exploratory data analysis
├── 2. Data Preparation # Data standardization & preprocessing
├── 3. Model Training # Model training & optimization
└── 4. Results Evaluation # Evaluation & visualization
Each phase is independent and reusable, enabling efficient iterations and simplified debugging.
✅ Robust Pipeline: End-to-end system from raw data to production-ready model
✅ Scalability: Efficient management of large-scale datasets
✅ Modularity: Reusable and well-structured components
✅ Documentation: Documented code and explanatory notebooks
✅ Best Practices: Implementation of MLOps principles and software engineering standards
This project demonstrates:
- Ability to manage a complete ML project lifecycle
- Deep understanding of computer vision and segmentation techniques
- Skills in complex data wrangling and preprocessing
- Implementation of production ML best practices
- Problem-solving in medical and scientific contexts
Deep Learning: Modern segmentation architectures, optimization techniques, loss functions
Data Science: Feature engineering, data validation, quality metrics
Software Engineering: Code organization, documentation, reproducibility
Cloud Computing: Virtualized environments, resource optimization
Visualization: Data-driven insights communication
This project consolidated expertise in:
- Transforming real-world problems into ML solutions
- Managing complexity of heterogeneous medical datasets
- Model optimization for performance and memory efficiency
- Deployment and scaling of AI systems
- Effective communication of technical results
If you use this project in your work, please cite:
@software{malaria_ai_2025,
title={MalariaAI: Advanced Deep Learning for Medical Image Analysis},
author={Vicio Di Cara},
year={2025},
url={https://github.com/VicDc}
}This project is licensed under the MIT License - see the LICENSE file for details.
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