Link paper and improve dataset card

README.md

@@ -1,4 +1,14 @@
 ---
+language:
+- en
+license: apache-2.0
+size_categories:
+- 100K<n<1M
+task_categories:
+- image-to-image
+- text-to-image
+- image-classification
+pretty_name: cgDDI
 dataset_info:
   features:
   - name: image
@@ -41,40 +51,44 @@ configs:
   data_files:
   - split: train
     path: data/train-*
-license: apache-2.0
-task_categories:
-- image-to-image
-- text-to-image
-- image-classification
-language:
-- en
 tags:
 - medical
-pretty_name: cgDDI
-size_categories:
-- 100K<n<1M
 ---
+
 # Dataset Card for cgDDI
 
-This repository is the official dataset of cgDDI: Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification which is currently under review. As this paper is not openly distributed (or on Arxiv), the method detail here will be minimal.
+This repository is the official dataset of [Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification](https://huggingface.co/papers/2607.12987).
+
+- **Code Repository:** [GitHub](https://github.com/hectorcarrion/ControllableGenDDI)
+- **Croissant File:** [croissant_rai.json](https://huggingface.co/datasets/hcarrion/ControllableGenDDI/resolve/main/croissant_rai.json)
 
 ## 🥐 Croissant File 🥐
 We host a dedicated and manually extended croissant file, expanding on the one automatically generated by HuggingFace. This file includes RAI fields and additional metadata useful to the research community. It is available on this repo, direct link here: https://huggingface.co/datasets/hcarrion/ControllableGenDDI/resolve/main/croissant_rai.json
 
-
 ## Dataset Details
 
-We do not re-host previous artifacts, thus remember to fetch the original DDI dataset at https://ddi-dataset.github.io/ and the sDDI masks from https://github.com/hectorcarrion/FEDD.
+We introduce **cgDDI** (**C**ontrollable **G**eneration of **D**iverse **D**ermatological **I**magery), a hybrid framework to synthesize realistic healthy skin samples, map rare lesions, and support fair classification. 
+
+The dataset contains **266,136 skin-tone-balanced synthetic images** across 65+ disease classes:
+- **Healthy synthetics** — 309 pixel-perfect in-distribution healthy skin images
+- **Lesion-mapped synthetics** — single-sample rare-disease augmentations
+- **Semantic synthetics** — textual-inversion + LoRA latent-diffusion samples
+- **Fairness labels** — Fitzpatrick skin-tone tags for stratified evaluation
 
-Further discussed on the manuscript. Soon to be added here post-review!
+*Note: We do not re-host previous artifacts, thus remember to fetch the original DDI dataset at https://ddi-dataset.github.io/ and the sDDI masks from https://github.com/hectorcarrion/FEDD.*
 
-## Code
+## Download / Sample Usage
 
-All code can be found on https://github.com/hectorcarrion/ControllableGenDDI along with a README.
+You can download the dataset via the Hugging Face CLI:
+
+```bash
+# Download dataset via the HF CLI
+hf download hcarrion/ControllableGenDDI --repo-type dataset --local-dir data/cgddi
+```
 
 ## Models
 
-Models can be found at the following links:
+We openly release **65 disease-conditioned LoRA checkpoints**, one per condition, on Hugging Face:
 
 1. https://huggingface.co/hcarrion/xanthogranuloma
 2. https://huggingface.co/hcarrion/verruciform_xanthoma
@@ -143,4 +157,17 @@ Models can be found at the following links:
 65. https://huggingface.co/hcarrion/abscess
 
 ## Usage Terms
-We release this data, code and models with the intent of academic use and to promote fairness research. We do not allow un-ethical usage of these artifacts.
+We release this data, code and models with the intent of academic use and to promote fairness research. We do not allow un-ethical usage of these artifacts.
+
+## Citation
+
+```bibtex
+@inproceedings{carrion2026cgddi,
+  title     = {Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification},
+  author    = {Carri{\\'o}n, H{\\'e}ctor and Norouzi, Narges},
+  booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
+  year      = {2026},
+  publisher = {Springer},
+  series    = {Lecture Notes in Computer Science}
+}
+```