Bring the studies contents in line with what actually ships
Browse files
DATA.md
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@@ -226,7 +226,7 @@ macro-mean, which runs 0.75–0.82 on the labelled cohorts.
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Contents: `tiles/` holds 109,904 224 px tile graphs with CellViT++ pseudo-labels (BACH 13,837,
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BRACS 95,670, BreakHis 397); `bach_cellvit/` holds 400 per-image pseudo-label CSVs (538,421
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cells, columns `centroid_x_px,centroid_y_px,cell_type,type_prob`) plus a summary JSON; and
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`
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The pseudo-labeller was validated on a held-out ground-truth split first: **ARI 0.560**
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(threshold 0.5), Cohen κ 0.709, per-class confusion diagonal 0.864 / 0.562 /
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@@ -258,9 +258,9 @@ The tuned protocol takes 5 of 6 columns. Against the frozen probe, which uses th
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so the comparison isolates the adapters, it gains +3.2 / +8.0 / +2.8 / +11.5 / +1.0 / +2.4.
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Contents: `graphs_v1/` holds NuCLS (1,694 `.pt` × 2 label granularities plus 5-fold split
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tables) and PanNuke (3 folds × 20×/40×, 14,414 `.pt`), both with 1-dim edges
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`source_optuna_hps/` holds the per-task tuned hyperparameters
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The v1 label CSVs keep stale absolute paths, which is harmless: both consumers glob the `.pt`
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files directly rather than reading `graph_path`. The split tables key on `slide_name`.
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@@ -282,7 +282,9 @@ the only model with a positive subtype silhouette, i.e. the only one separating
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all. `p < 0.001` rather than 0: the estimator is `mean(null ≤ observed)` over 1,000
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permutations, so 0 is a resolution floor.
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### `studies/preprocessing_runtime/`: cost of building the graphs
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Contents: `tiles/` holds 109,904 224 px tile graphs with CellViT++ pseudo-labels (BACH 13,837,
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BRACS 95,670, BreakHis 397); `bach_cellvit/` holds 400 per-image pseudo-label CSVs (538,421
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cells, columns `centroid_x_px,centroid_y_px,cell_type,type_prob`) plus a summary JSON; and
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`paper_metrics/` holds the pooled edge-homophily and pseudo-label-gate results as JSON.
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The pseudo-labeller was validated on a held-out ground-truth split first: **ARI 0.560**
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(threshold 0.5), Cohen κ 0.709, per-class confusion diagonal 0.864 / 0.562 /
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so the comparison isolates the adapters, it gains +3.2 / +8.0 / +2.8 / +11.5 / +1.0 / +2.4.
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Contents: `graphs_v1/` holds NuCLS (1,694 `.pt` × 2 label granularities plus 5-fold split
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tables) and PanNuke (3 folds × 20×/40×, 14,414 `.pt`), both with 1-dim edges, and
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`source_optuna_hps/` holds the per-task tuned hyperparameters (the search was unseeded, so
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these values cannot be recovered by re-running it).
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The v1 label CSVs keep stale absolute paths, which is harmless: both consumers glob the `.pt`
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files directly rather than reading `graph_path`. The split tables key on `slide_name`.
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all. `p < 0.001` rather than 0: the estimator is `mean(null ≤ observed)` over 1,000
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permutations, so 0 is a resolution floor.
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This study has no data directory of its own: it runs on `baselines/` and
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`labels/tcga_brca_slide_labels.csv`, both of which ship here, and the analysis code is in the
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code repository.
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### `studies/preprocessing_runtime/`: cost of building the graphs
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