From model to working application
I built OncoEdge as an oral-lesion imaging research project, combining a fine-tuned YOLO detector with BioMedCLIP image comparisons. The public demo runs on a CPU and exposes region overlays, relative scores, model disagreement, and structured results.
Working within constraints
The edge-deployment work explores fixed-text embedding caching, a vision-only ONNX export, and TensorRT tooling for a smaller inference path. Jetson latency and final TensorRT engine size remain unverified; the hosted demo demonstrates the CPU implementation.
Making uncertainty visible
The engineering work includes evaluation, image-preprocessing checks, and explicit abstention handling. These details matter when several models are combined and their outputs can disagree.
This is a research demonstration, not a clinical diagnostic tool. The demo and repository document its limitations.