case

README.md

@@ -32,18 +32,18 @@ library_name: timm
 ---
 
 # ♆ Patho-Bench
-[📄 Preprint](https://arxiv.org/pdf/2502.06750) | [Code](https://github.com/mahmoodlab/patho-bench)
+[📄 Preprint](https://arxiv.org/pdf/2502.06750) | [Code](https://github.com/mahmoodlab/Patho-Bench)
 
 <img src="patho_bench_public.png" alt="Patho-Bench" style="width: 38%;" align="right"/>
 
 **Patho-Bench is designed for high-throughput evaluations of patch and slide encoder foundation models for whole-slide images (WSIs).** 
 
-This HuggingFace repository contains the data splits for the public Patho-Bench tasks. Please visit our codebase on [GitHub](https://github.com/mahmoodlab/patho-bench) for the full codebase and benchmark implementation.
+This HuggingFace repository contains the data splits for the public Patho-Bench tasks. Please visit our codebase on [GitHub](https://github.com/mahmoodlab/Patho-Bench) for the full codebase and benchmark implementation.
 
 This project was developed by the [Mahmood Lab](https://faisal.ai/) at Harvard Medical School and Brigham and Women's Hospital.
 
 > [!NOTE]
-> Contributions are welcome! If you'd like to submit a new dataset and/or task for inclusion in Patho-Bench, please reach out to us via the [Issues](https://github.com/mahmoodlab/patho-bench/issues) tab of our Github repo.
+> Contributions are welcome! If you'd like to submit a new dataset and/or task for inclusion in Patho-Bench, please reach out to us via the [Issues](https://github.com/mahmoodlab/Patho-Bench/issues) tab of our Github repo.
 
 Currently, Patho-Bench contains the following task families. We will add more tasks in the future. For further details on each task, please refer to the [THREADS foundation model paper](https://arxiv.org/abs/2501.16652).
 
@@ -75,7 +75,7 @@ login(token="YOUR_HUGGINGFACE_TOKEN")
 
 ## ⬇️ Usage
 
-The Patho-Bench data splits are designed for use with the Patho-Bench [software package](https://github.com/mahmoodlab/patho-bench). However, you are welcome to use the data splits in your custom pipeline. Each task is associated with a YAML file containing task metadata and a CSV file containing the sample IDs, slide IDs, and labels.
+The Patho-Bench data splits are designed for use with the Patho-Bench [software package](https://github.com/mahmoodlab/Patho-Bench). However, you are welcome to use the data splits in your custom pipeline. Each task is associated with a YAML file containing task metadata and a CSV file containing the sample IDs, slide IDs, and labels.
 
 > [!NOTE]
 > Patho-Bench only provides the data splits and labels, NOT the raw image data. You will need to download the raw image data from the respective dataset repositories (see links below).
@@ -86,7 +86,7 @@ import datasets
 dataset='cptac_coad'
 task='KRAS_mutation'
 datasets.load_dataset(
-                    'MahmoodLab/patho-bench', 
+                    'MahmoodLab/Patho-Bench', 
                     cache_dir='/path/to/saveto',
                     dataset_to_download=dataset,     # Throws error if source not found
                     task_in_dataset=task,            # Throws error if task not found in dataset
@@ -100,7 +100,7 @@ import datasets
 dataset='cptac_coad'
 task='*'
 datasets.load_dataset(
-                    'MahmoodLab/patho-bench', 
+                    'MahmoodLab/Patho-Bench', 
                     cache_dir='/path/to/saveto',
                     dataset_to_download=dataset,     
                     task_in_dataset=task,            
@@ -113,7 +113,7 @@ datasets.load_dataset(
 import datasets
 dataset='*'
 datasets.load_dataset(
-                    'MahmoodLab/patho-bench', 
+                    'MahmoodLab/Patho-Bench', 
                     cache_dir='/path/to/saveto',
                     dataset_to_download=dataset,
                     trust_remote_code=True