Update README.md

README.md

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 #### Overview
 BioMed-VITAL is a multimodal foundation model specifically tuned for biomedical applications. It leverages visual and textual data to improve understanding and reasoning within the biomedical domain.
 
-![Biomed-VITAL Framework Overview](https://raw.githubusercontent.com/BioMed-VITAL/BioMed-VITAL.github.io/main/images/updated_instruction_data_framework_00.png) 
-
 #### Model Training
 The training of BioMed-VITAL involved two key stages, both incorporating clinician preferences to ensure the relevance and quality of the training data:
 
@@ -17,14 +15,4 @@ The training of BioMed-VITAL involved two key stages, both incorporating clinici
 The effectiveness of BioMed-VITAL was demonstrated through significant improvements in two key areas:
 
 - **Open Visual Chat:** The model showed a relative improvement of 18.5%, indicating enhanced capabilities in engaging in visual dialogues pertinent to biomedical contexts.
-- **Medical Visual Question Answering (VQA):** BioMed-VITAL achieved a win rate of up to 81.73% in this domain, showcasing its superior performance in interpreting and responding to complex medical imagery and queries.
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-## Case Study
-### Biomedical Visual Instruction-Following Example
-![](https://raw.githubusercontent.com/BioMed-VITAL/BioMed-VITAL.github.io/main/images/case_update2_00.png) 
-### Biomedical VQA Benchmark
-![](https://raw.githubusercontent.com/BioMed-VITAL/BioMed-VITAL.github.io/main/images/case5_updated_00.png) 
-### Clinician Annotation Examples
-![](https://raw.githubusercontent.com/BioMed-VITAL/BioMed-VITAL.github.io/main/images/appendixH_00.png) 
-
-For more information, access to the dataset, and to contribute, please visit our [GitHub repository](https://github.com/yourrepo/biomed-vital).
+- **Medical Visual Question Answering (VQA):** BioMed-VITAL achieved a win rate of up to 81.73% in this domain, showcasing its superior performance in interpreting and responding to complex medical imagery and queries.