Reading the geometry of AS-OCT pretraining
Measured representation changes across five AS-OCT pretraining runs, and the profiling work that cut two-GPU JEPA update time by 14.2%.
Research engineering, worked through.
Experiments, measured results, and the decisions they inform.
Measured representation changes across five AS-OCT pretraining runs, and the profiling work that cut two-GPU JEPA update time by 14.2%.
7 connected articles. Read them in order, or start with the problem that interests you.
The learning objectives I use for AS-OCT: reconstruct hidden pixels, align views of an image, and preserve variation between sources.
The five AS-OCT recipes I implemented on 3.43 million images: their inputs, objectives, shared controls, and compute tradeoffs.
How I built OphtaFlow AI for diagnostic support in production 24/7, powered by CorneaForge, and the next stage of its clinical and research platform.
How I turn native corneal samples into maps and Zernike descriptors in CorneaForge, with explicit reference surfaces, coordinates and units.
How I accelerate CorneaForge’s geometry engine with reusable interpolation, batched surface fits, and directly evaluated derivatives.
How I built the optical calculation in CorneaForge: surface slopes, refraction, ray intersections and optical path differences become Zernike descriptors.
How I implemented conoid and biconic fitting in CorneaForge, and why choosing a reference determines which parts of corneal shape remain in the residual.
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