Spaces:
Sleeping
Sleeping
Polish card copy
Browse files
README.md
CHANGED
|
@@ -13,18 +13,18 @@ short_description: Frozen V-JEPA-2 predicts cell-cycle state in microscopy
|
|
| 13 |
|
| 14 |
# Temporal Cell State Prediction in Microscopy
|
| 15 |
|
| 16 |
-
A frozen **V-JEPA-2 ViT-L** encoder
|
| 17 |
-
cell-cycle state (interphase
|
| 18 |
-
clips
|
| 19 |
-
pipeline with a single clip
|
| 20 |
|
| 21 |
-
Pick a single-cell clip and the model classifies it into its cell-cycle state
|
| 22 |
-
as **predicted vs actual**. The selector tags each clip β
correct
|
| 23 |
you can inspect both. Held-out test-set metrics (n=5,312) are in the collapsible panel.
|
| 24 |
|
| 25 |
**Study finding:** in this small-data regime, **data scaling, not model scaling, is the
|
| 26 |
-
binding constraint**
|
| 27 |
-
macro-F1 far more than
|
| 28 |
|
| 29 |
- 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)
|
| 30 |
-
- Data: MICCAI Cell Tracking Challenge
|
|
|
|
| 13 |
|
| 14 |
# Temporal Cell State Prediction in Microscopy
|
| 15 |
|
| 16 |
+
A frozen **V-JEPA-2 ViT-L** encoder with a small attentive-pool head predicts per-cell
|
| 17 |
+
cell-cycle state (interphase, pre-mitosis, mitosis) directly from short single-cell
|
| 18 |
+
clips. It replaces the *classify* stage of the conventional segment, track, classify
|
| 19 |
+
pipeline with a single clip-to-state model.
|
| 20 |
|
| 21 |
+
Pick a single-cell clip and the model classifies it into its cell-cycle state, shown
|
| 22 |
+
as **predicted vs actual**. The selector tags each clip β
correct or β misclassified so
|
| 23 |
you can inspect both. Held-out test-set metrics (n=5,312) are in the collapsible panel.
|
| 24 |
|
| 25 |
**Study finding:** in this small-data regime, **data scaling, not model scaling, is the
|
| 26 |
+
binding constraint.** Moving the baseline from a small to a larger labelled dataset lifts
|
| 27 |
+
macro-F1 far more than a roughly 80x larger encoder does.
|
| 28 |
|
| 29 |
- 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)
|
| 30 |
+
- Data: MICCAI Cell Tracking Challenge, Fluo-N2DL-HeLa. Labels derived from lineage trees (no manual annotation).
|