---
title: Temporal State Prediction
emoji: 🔬
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: mit
short_description: Frozen V-JEPA-2 predicts cell-cycle state in microscopy
---

# Temporal Cell State Prediction in Microscopy

A frozen **V-JEPA-2 ViT-L** encoder + a small attentive-pool head predicts per-cell
cell-cycle state (interphase / pre-mitosis / mitosis) directly from short single-cell
clips — replacing the *classify* stage of the conventional segment → track → classify
pipeline with a single clip → state model.

Pick a single-cell clip and the model classifies it into its cell-cycle state — shown
as **predicted vs actual**. The selector tags each clip ✅ correct / ❌ misclassified so
you can inspect both. Held-out test-set metrics (n=5,312) are in the collapsible panel.

**Study finding:** in this small-data regime, **data scaling, not model scaling, is the
binding constraint** — moving the baseline from a small to a larger labelled dataset lifts
macro-F1 far more than an ~80× larger encoder does.

- Models: [`DnaRnaProteins/vjepa2-cell-cycle-vit-l`](https://huggingface.co/DnaRnaProteins/vjepa2-cell-cycle-vit-l), [`DnaRnaProteins/unet-bilstm-cell-cycle-baseline`](https://huggingface.co/DnaRnaProteins/unet-bilstm-cell-cycle-baseline)
- Data: MICCAI Cell Tracking Challenge — Fluo-N2DL-HeLa. Labels derived from lineage trees (no manual annotation).