Notes on medical-imaging AI, production ML systems, and what I've learned shipping research-grade work into clinical workflows. Three sections, three formats.
Narrative deep-dives on production medical-imaging AI.
Three detection architectures fused into one ensemble — what each model contributes, what fusion actually buys, and the engineering numbers from the arXiv paper.
Almost ungoogleable combo — Electron + PyInstaller + AES-256 model decryption + admin-aware auto-updater — and the interactions between pieces are where everything goes wrong.
What actually dominated my first year as an AI Scientist — and it wasn't the model architectures.
Lab notes: setup → method → results → verdict.
Educational primers, referenced from deeper posts.