Keep dataset card focused on data contents and usage

README.md

@@ -20,7 +20,7 @@ pretty_name: 'HyperMIL: Whole-Slide Histopathology Features and Graph Representa
 
 This dataset provides **pre-extracted patch features, aligned patch coordinates, and precomputed graph and group representations** for 1,344 diagnostic whole-slide images (WSIs) from four TCGA cancer cohorts. It supports survival prediction with multiple-instance learning, graph neural networks, and hypergraph neural networks.
 
-The data accompany **HyperMIL: Adaptive Spatial–Semantic Hypergraph MIL for Survival Prediction from Whole-Slide Histopathology**. They provide the prepared inputs for evaluating HyperMIL, its ablations, and the baseline methods in the shared [code framework](https://github.com/shannonchen37/HyperMIL). **Reproducing the released results does not require extracting features or rebuilding graphs.**
+The data accompany the [HyperMIL code repository](https://github.com/shannonchen37/HyperMIL). The supplied feature and graph files can be loaded directly by its training and evaluation scripts, without rerunning preprocessing.
 
 ## Cohorts
 
@@ -47,7 +47,7 @@ Sizes use decimal GB. The release contains **12,096 NumPy files**. Overall-survi
 
 `N` is the number of retained patches in a WSI and varies between slides. Features are stored once and shared across methods; graph files refer to that same patch ordering. PatchGCN graph files store connectivity without duplicating the feature arrays.
 
-**MGAS** captures spatial tissue organization through overlapping structural hyperedges at multiple granularities. **PASG** captures feature-space relationships through sparse, weighted patch-to-prototype connections. Both representations are provided for **all four cohorts**.
+Both MGAS and PASG graph files are provided for **all four cohorts**.
 
 ### Directory layout
 
@@ -96,7 +96,7 @@ python scripts/download_models.py --cohort STAD
 python scripts/evaluate.py --config configs/hypermil.yaml --cohort STAD
 ```
 
-Use `STAD`, `THCA`, `CHOL`, or `LIHC` to select a cohort. Omit `--cohort` from both download commands to obtain all four cohorts, then run `python scripts/reproduce.py` to evaluate the main comparison and ablation tables. Downloads resume when the same command is rerun. The supplied layout requires no manual path edits or Hugging Face login.
+Use `STAD`, `THCA`, `CHOL`, or `LIHC` to select a cohort. Omit `--cohort` from both download commands to obtain all four cohorts. Downloads resume when the same command is rerun. The supplied layout requires no manual path edits or Hugging Face login.
 
 Methods are selected through YAML configurations within the same training and evaluation framework. Keep feature ordering, graph inputs, patient splits, and method configurations together when using the released checkpoints. See the [GitHub README](https://github.com/shannonchen37/HyperMIL#quick-start) for installation and evaluation details.