Update README.md

README.md

@@ -31,11 +31,9 @@ license: mit
 - **Paper [optional]:** https://arxiv.org/abs/2412.13126
 - **Demo [optional]:** [More Information Needed]
 
-## Uses
-
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### Direct Use
+## Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
@@ -69,9 +67,7 @@ text_feature = model.encode_text(token_input)
 
 <!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
-
-#### Testing Data
+### Testing Data
 
 <!-- This should link to a Dataset Card if possible. -->
 
@@ -111,7 +107,7 @@ We present benchmark results for a range of representative tasks. A complete set
 | CPTAC-NSCLC | 0.647 | 0.607 | 0.643 | 0.836 | **0.863** |
 | EBRAINS | 0.096 | 0.093 | 0.325 | 0.371 | **0.456** |
 
-#### Summary
+### Summary
 
 Validated on 18 diverse benchmarks with more than 14,000 whole slide images (WSIs), KEEP achieves state-of-the-art performance in zero-shot cancer diagnostic tasks. Notably, for cancer detection, KEEP demonstrates an average sensitivity of 89.8% at a specificity of 95.0% across 7 cancer types, significantly outperforming vision-only foundation models and highlighting its promising potential for clinical application. For cancer subtyping, KEEP achieves a median balanced accuracy of 0.456 in subtyping 30 rare brain cancers, indicating strong generalizability for diagnosing rare tumors.
 
@@ -120,7 +116,6 @@ Validated on 18 diverse benchmarks with more than 14,000 whole slide images (WSI
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
-**BibTeX:**
 ```
 @article{zhou2024keep,
   title={A Knowledge-enhanced Pathology Vision-language Foundation Model for Cancer Diagnosis},