Add pipeline_tag and improve model card metadata

README.md

@@ -1,12 +1,10 @@
 ---
-license: cc-by-nc-4.0
-task_categories:
-- image-segmentation
 language:
 - en
+license: cc-by-nc-4.0
+pipeline_tag: image-segmentation
 tags:
 - medical-imaging
-- image-segmentation
 - vision-language-models
 - clip
 - unimedclip
@@ -23,7 +21,11 @@ tags:
 <a href="https://huggingface.co/TahaKoleilat/MedCLIPSeg" target="_blank"><img alt="HuggingFace Models" src="https://img.shields.io/badge/Models-Reproduce-2ea44f?logo=huggingface&logoColor=white" height="25"/></a>
 <a href="#citation"><img alt="Citation" src="https://img.shields.io/badge/Citation-BibTeX-6C63FF?logo=bookstack&logoColor=white" height="25"/></a>
 
-This repository hosts the **official trained model checkpoints** for **MedCLIPSeg**, a vision–language framework for **medical image segmentation** built on top of **CLIP**.
+This repository hosts the **official trained model checkpoints** for **MedCLIPSeg**, presented in the paper [MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation](https://huggingface.co/papers/2602.20423).
+
+**Authors:** Taha Koleilat, Hojat Asgariandehkordi, Omid Nejati Manzari, Berardino Barile, Yiming Xiao, Hassan Rivaz.
+
+MedCLIPSeg is a vision–language framework for **medical image segmentation** built on top of **CLIP**. It adapts CLIP for robust, data-efficient, and uncertainty-aware segmentation through probabilistic cross-modal attention and bidirectional interaction between image and text tokens.
 
 The released checkpoints correspond exactly to the experiments reported in our paper and are provided **for evaluation and reproducibility purposes only**.