Update README.md

README.md

@@ -54,7 +54,7 @@ transform = transforms.Compose([
     transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
 ])
 
-example_image_path = './quick_start/example.tif'
+example_image_path = './example.tif'
 example_text = ['an H&E image of breast invasive carcinoma.', 'an H&E image of normal tissue.', 'an H&E image of lung adenocarcinoma.']
 
 img_input =  transform(Image.open(example_image_path).convert('RGB')).unsqueeze(0)
@@ -64,63 +64,6 @@ img_feature = model.encode_image(img_input)
 text_feature = model.encode_text(token_input)
 
 ```
-<!-- 
-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-[More Information Needed]
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-## Training Details
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-### Training Data
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-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-[More Information Needed]
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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 ## Evaluation
 
@@ -134,18 +77,7 @@ Use the code below to get started with the model.
 
 We present benchmark results for a range of representative tasks. A complete set of benchmarks can be found in the [paper](https://arxiv.org/abs/2412.18***). These results will be updated with each new iteration of KEEP. 
 
-<!-- #### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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 ### Results
 
 
@@ -183,42 +115,6 @@ We present benchmark results for a range of representative tasks. A complete set
 
 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.
 
-<!-- 
-## Model Examination [optional]
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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 ## Citation [optional]
 
@@ -232,25 +128,3 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
   journal={arXiv preprint arXiv:2412.13126},
   year={2024}
 }
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-**APA:**
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