Update README.md

README.md

@@ -48,8 +48,8 @@ license: cc-by-nc-nd-4.0
 # Dataset Card for The Cancer Genome Atlas (TCGA) Multimodal Dataset
 <!-- Provide a quick summary of the dataset. -->
 
-The Cancer Genome Atlas (TCGA) Multimodal Dataset is a comprehensive collection of clinical data, pathology reports, and slide images for cancer patients. 
-This dataset aims to facilitate research in multimodal machine learning for oncology by providing embeddings generated using state-of-the-art models such as GatorTron and UNI.
+The Cancer Genome Atlas (TCGA) Multimodal Dataset is a comprehensive collection of clinical data, pathology reports, slide images, molecular data, and radiology images for cancer patients. 
+This dataset aims to facilitate research in multimodal machine learning for oncology by providing embeddings generated using state-of-the-art models including GatorTron, MedGemma, Qwen, Llama, UNI, SeNMo, REMEDIS, and RadImageNet.
 
 - **Curated by:** Lab Rasool
 - **Language(s) (NLP):** English
@@ -61,13 +61,27 @@ This dataset aims to facilitate research in multimodal machine learning for onco
 ```python
 from datasets import load_dataset
 
-clinical_dataset = load_dataset("Lab-Rasool/TCGA", "clinical", split="gatortron")
-biobert_clinical_dataset = load_dataset("Lab-Rasool/TCGA", "clinical", split="biobert")
-pathology_report_dataset = load_dataset("Lab-Rasool/TCGA", "pathology_report", split="gatortron")
+# Clinical data embeddings (4 models available)
+clinical_gatortron = load_dataset("Lab-Rasool/TCGA", "clinical", split="gatortron")
+clinical_medgemma = load_dataset("Lab-Rasool/TCGA", "clinical", split="medgemma")
+clinical_qwen = load_dataset("Lab-Rasool/TCGA", "clinical", split="qwen")
+clinical_llama = load_dataset("Lab-Rasool/TCGA", "clinical", split="llama")
+
+# Pathology report embeddings (4 models available)
+pathology_gatortron = load_dataset("Lab-Rasool/TCGA", "pathology_report", split="gatortron")
+pathology_medgemma = load_dataset("Lab-Rasool/TCGA", "pathology_report", split="medgemma")
+pathology_qwen = load_dataset("Lab-Rasool/TCGA", "pathology_report", split="qwen")
+pathology_llama = load_dataset("Lab-Rasool/TCGA", "pathology_report", split="llama")
+
+# Whole slide image embeddings
 wsi_dataset = load_dataset("Lab-Rasool/TCGA", "wsi", split="uni")
+
+# Molecular data embeddings
 molecular_dataset = load_dataset("Lab-Rasool/TCGA", "molecular", split="senmo")
-remedis_radiology_dataset = load_dataset("Lab-Rasool/TCGA", "radiology", split="remedis")
-radimagenet_radiology_dataset = load_dataset("Lab-Rasool/TCGA", "radiology", split="radimagenet")
+
+# Radiology embeddings (2 models available)
+radiology_remedis = load_dataset("Lab-Rasool/TCGA", "radiology", split="remedis")
+radiology_radimagenet = load_dataset("Lab-Rasool/TCGA", "radiology", split="radimagenet")
 ```
 
 Example code for loading HF dataset into a PyTorch Dataloader.
@@ -95,20 +109,49 @@ class CustomDataset(Dataset):
 
 if __name__ == "__main__":
     
-    clinical_dataset = load_dataset("Lab-Rasool/TCGA", "clinical", split="gatortron")
+    # Load clinical embeddings from different models
+    clinical_gatortron = load_dataset("Lab-Rasool/TCGA", "clinical", split="gatortron")
+    clinical_llama = load_dataset("Lab-Rasool/TCGA", "clinical", split="llama")
     wsi_dataset = load_dataset("Lab-Rasool/TCGA", "wsi", split="uni")
     
-    for index, item in enumerate(clinical_dataset):
-        print(np.frombuffer(item.get("embedding"), dtype=np.float32).reshape(item.get("embedding_shape")).shape)
+    # Example: Access embeddings
+    for index, item in enumerate(clinical_gatortron):
+        embedding = np.frombuffer(item.get("embedding"), dtype=np.float32).reshape(item.get("embedding_shape"))
+        print(f"GatorTron embedding shape: {embedding.shape}")  # Shape: (1024,)
+        break
+        
+    for index, item in enumerate(clinical_llama):
+        embedding = np.frombuffer(item.get("embedding"), dtype=np.float32).reshape(item.get("embedding_shape"))
+        print(f"Llama embedding shape: {embedding.shape}")  # Shape: (2304,)
         break
 ``` 
 
+## Dataset Statistics
+
+### Clinical Data
+- **10,771 patient records** per model
+- **113 columns** including clinical metadata and embeddings
+- **Embedding dimensions:**
+  - GatorTron: 1024
+  - MedGemma: 2560
+  - Qwen: 1024
+  - Llama: 2304
+
+### Pathology Reports
+- **10,857 patient records** per model
+- **17 columns** including pathology metadata and embeddings
+- **Embedding dimensions:**
+  - GatorTron: 1024
+  - MedGemma: 2560
+  - Qwen: 1024
+  - Llama: 2304
+
 ## Dataset Creation
 
 #### Data Collection and Processing
 The raw data for this dataset was acquired using MINDS, a multimodal data aggregation tool developed by Lab Rasool. 
 The collected data includes clinical information, pathology reports, and whole slide images from The Cancer Genome Atlas (TCGA). 
-The embeddings were generated using the HoneyBee embedding processing tool, which utilizes foundational models such as GatorTron and UNI.
+The embeddings were generated using the HoneyBee embedding processing tool, which utilizes foundational models such as GatorTron, MedGemma, Qwen, Llama, and UNI.
 
 #### Who are the source data producers?
 The source data for this dataset was originally collected and maintained by The Cancer Genome Atlas (TCGA) program, a landmark cancer genomics project jointly managed by the National Cancer Institute (NCI).