I specialize in machine learning research and development within the healthcare sector, with over five years of experience in medical imaging. I create research prototypes and internal-use APIs, delivered as Dockerized FastAPI services deployed in your cloud or Virtual Private Cloud (VPC).

No clinical or diagnostic claims are made.

Capabilities include:

• Medical imaging tasks: classification, segmentation, detection (compatible with DICOM/PACS)
• Clinical Natural Language Processing (NLP): de-identification, report parsing, summarization, Retrieval-Augmented Generation (RAG) chatbots
• Generative and augmentation methods: ethical image synthesis for research and development
• Interpretability techniques: Grad-CAM, saliency maps, SHAP, feature importance analysis

What is provided:

• A model trained using your data (or public/synthetic data), accompanied by clear performance metrics
• Reproducible code and notebooks, a README file, and a model card
• Basic monitoring, logging, and guidance for integration
• Practical recommendations to enhance your machine learning pipeline and integration process
• Reports detailing model performance, accuracy, and efficiency
• A Dockerized FastAPI inference service

The workflow involves:

1. Defining scope and success criteria (task, metric, constraints)
2. Data audit and preprocessing (with a focus on privacy)
3. Model training and evaluation (including tuned baselines)
4. API packaging
5. Handover, documentation, and outlining next steps

Deliverables are strictly for research and development or internal evaluation purposes only; they are not medical devices and are not intended for clinical use.