Learning 3D biophysical cell properties from 2D images and cell-population statistics
Inferring 3D cellular properties from 2D microscopy is difficult when a reference instrument reports only population statistics rather than labels for individual cells. Here we develop a population-supervised framework that maps single 2D red-cell images to latent biophysical quantities and aggregates them to mean corpuscular volume, red-cell distribution width and mean corpuscular haemoglobin. The model combines shared local inference, a biophysically structured decoder for volume and haemoglobin, learned instance weighting and device-specific calibration. We formalise conditions under which aggregate observations identify restricted instance predictors, show why population agreement doe...
| Authors | Santiago Hernández-Orozco, Hector Zenil |
|---|---|
| Primary category | cs.AI |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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