AI Advisor for Medical Imaging

End-to-end guidance for the effort to bring a medical imaging workflow from raw endoscopy data to reproducible, production-oriented model development while respecting strict confidentiality constraints.

Scope (NDA-safe)

• Built a data ingestion pipeline from cloud object storage to training-ready datasets.
• Set up experiment tracking and reproducibility workflows for model development.
• Adapted self-supervised and transfer-learning strategies for domain-specific imagery.
• Managed cloud GPU resources to optimize training throughput and cost-efficiency.
• Planned deployment pathways for edge-oriented inference environments.

Stack

• Python, PyTorch, and foundation-model workflows.
• Weights & Biases for tracking and experiment management.
• S3-backed datasets and cloud GPU compute.

Engineering Challenges

• Handling high-volume, heterogeneous video data under medical data governance requirements.
• Reducing bottlenecks between storage, preprocessing, and GPU training jobs.
• Keeping experiments reproducible across rapid model iterations and changing dataset versions.

Outcome

• Faster experimentation cycles with stronger observability and version traceability.
• Reusable training setup that can scale across future medical imaging tasks.
• Foundation for real-time, clinically relevant inference pipelines.