Update README.md

README.md

@@ -13,7 +13,12 @@ pipeline_tag: visual-question-answering
 # Med-R1
 Med-R1 is a reinforcement learning (RL)-enhanced vision-language model (VLM) designed for medical reasoning across 8 imaging modalities (CT, MRI, Ultrasound, Dermoscopy, Fundus Photography, Optical Coherence Tomography (OCT), Microscopy, and X-ray) and 5 key tasks (modality recognition, anatomy identification, disease diagnosis, lesion grading, and biological attribute analysis). Using Group Relative Policy Optimization (GRPO), Med-R1 improves generalization and trustworthiness, surpassing Qwen2-VL-2B by 29.94% and even outperforming the much larger Qwen2-VL-72B. Our model checkpoints provide researchers with a powerful tool for advancing medical AI with RL-driven enhancements.
 
-## Start with the Model
+## Description of Models
 
+- **Cross-Modality**: We provide checkpoints trained separately on the following modalities:
+  - **CT**, **MRI**, **X-Ray**, **Fundus (FP)**, **Dermoscopy (Der)**, **Microscopy (Micro)**, **OCT**, and **Ultrasound (US)**.
+
+- **Cross-Task Learning**: We provide checkpoints trained separately on the following tasks:
+  - **Anatomy Identification**, **Disease Diagnosis**, **Lesion Grading**, **Modality Recognition**, and **Biological Attribute Analysis**.
 
 ## Citation