Improve dataset card: add task categories, paper link and usage instructions

README.md

@@ -1,20 +1,66 @@
 ---
 license: mit
+task_categories:
+- image-segmentation
+tags:
+- medical
+- cancer-screening
+- pan-cancer
 ---
 
-Linshan Wu, Jiaxin Zhuang, and <a href="https://scholar.google.com/citations?hl=en&user=Z_t5DjwAAAAJ">**Hao Chen**</a>. **"Glance and Focus Reinforcement for Pan-cancer Screening"**. ICLR 2026.
+# Pancancer Dataset
 
-Code link: https://github.com/Luffy03/GF-Screen
+[**Paper**](https://huggingface.co/papers/2601.19103) | [**Code**](https://github.com/Luffy03/GF-Screen)
 
+This repository contains the dataset for the paper **"Glance and Focus Reinforcement for Pan-cancer Screening"**, accepted at ICLR 2026.
+
+GF-Screen is a Glance and Focus reinforcement learning framework for pan-cancer screening in large-scale CT scans. It employs a Glance model to localize diseased regions and a Focus model to precisely segment lesions, addressing the challenge of localizing diverse types of tiny lesions in large CT volumes.
 
 ## Download Dataset
+
+You can download the dataset using the Hugging Face CLI:
+```bash
+huggingface-cli download linshangmail/Pancancer --repo-type dataset --local-dir . --cache-dir ./cache
+```
+
+## Data Preparation
+
+Following the official repository structure, the data should be organized as follows:
+```
+./ # project root
+├──data
+    ├──imagesTr
+    ├──labelsTr
+    ├──external
+    ├──...
+├──jsons
+├──models
+├──...
+```
+
+## Usage
+
+### Training
+Training requires one 80G GPU:
+```bash
+bash GF_RL_Screen.sh
 ```
-huggingface-cli download linshangmail/Pancancer  --repo-type dataset --local-dir .  --cache-dir ./cache
+
+### Evaluation
+```bash
+# Internal validation:
+python --trained_pth $YOUR_MODEL_PATH val_GF_internal.py
+
+# External validation:
+python --trained_pth $YOUR_MODEL_PATH --dataset_name FLARE23 val_GF_external.py
+
+# FLARE prediction:
+python --trained_pth $YOUR_MODEL_PATH --test_data_path $YOUR_PATH_TO_FLARE_VALIDATION_IMAGES val_GF_internal.py
 ```
 
 ## Citation
 
-If you find this repo useful for your research, please consider citing the paper as follows:
+If you find this repo or dataset useful for your research, please consider citing the paper as follows:
 
 ```bibtex
 @inproceedings{GF-Screen,
@@ -23,6 +69,7 @@ If you find this repo useful for your research, please consider citing the paper
   booktitle={ICLR},
   year={2026},
 }
+
 @article{ma2024flare,
   title={Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge},
   author={Ma, Jun and Zhang, Yao and Gu, Song and Ge, Cheng and Mae, Shihao and Young, Adamo and Zhu, Cheng and Yang, Xin and Meng, Kangkang and Huang, Ziyan and others},
@@ -33,4 +80,4 @@ If you find this repo useful for your research, please consider citing the paper
   year={2024},
   publisher={Elsevier}
 }
-```
+```