Update README.md

README.md

@@ -14,7 +14,7 @@ tags:
 Attention-based multiple instance learning (ABMIL) models from Kao TW, Vremenko D, et al.,
 *Demographic Differences in Cancer Tissue Architecture Drive Disparities in Pathology AI
 Performance* (Cancer Cell, in revision). Code and usage: https://github.com/hms-dbmi/UNVEIL.
-Weights: **[HF_REPO_URL]**.
+Weights: (https://huggingface.co/yulab/UNVEIL).
 
 The models take tile embeddings of H&E whole-slide images (WSIs) from one of four public
 pathology foundation models (FMs), not pixels: CHIEF (768-dimensional), UNI (1024), GigaPath
@@ -123,7 +123,7 @@ interchangeable. Mitigation was externally evaluated on DFCI and CPTAC only.
 
 ```bash
 pip install huggingface_hub
-python -c "from huggingface_hub import snapshot_download; snapshot_download('[HF_REPO_ID]', local_dir='weights')"
+python -c "from huggingface_hub import snapshot_download; snapshot_download('yulab/UNVEIL', local_dir='weights')"
 
 # score slides with a task model (repository module 2)
 python 02_pathology_task_prediction/predict.py --manifest manifest.csv \
@@ -143,11 +143,9 @@ README gives the manifest and label formats.
 Weights: CC BY-NC 4.0. Code: GNU AGPL v3.0. Non-commercial academic use only.
 
 ```
-Kao TW, Vremenko D, Lin SY, Su FY, Li B, Tsai PC, Lee TH, Wang X, Yang S, Li JJX, Lo YC,
-Lin SC, Sholl L, Meredith DM, Ligon KL, Lin NU, Nasrallah MP, Golden JA, Meyerhardt J,
-Ogino S, Roetzer-Pejrimovsky T, Woehrer A, Signoretti S, Schnitt S, Yu KH.
-Demographic Differences in Cancer Tissue Architecture Drive Disparities in Pathology AI
-Performance. Cancer Cell (in revision). https://github.com/hms-dbmi/UNVEIL
+Kao TW, Vremenko D, et al. Demographic Differences in Cancer Tissue Architecture Drive
+Disparities in Pathology AI Performance. Cancer Cell (in revision).
+https://github.com/hms-dbmi/UNVEIL
 ```
 
 Contact: Kun-Hsing Yu, kun-hsing_yu@hms.harvard.edu.