2
stars
4
commits
2
linked in READMEs
Aug 15, 2025
updated
Code: https://github.com/UCSC-VLAA/MedVLThinker Project Page: https://ucsc-vlaa.github.io/MedVLThinker/
Our project provides several curated datasets for medical vision-language understanding and training:
| Dataset | Modality | Description | Download |
|---|---|---|---|
| MedVLThinker-m23k-tokenized | Text-only | Tokenized version of the m23k dataset | 🤗 HF |
| MedVLThinker-pmc_vqa-gpt_4o_reasoning-tokenized | Image-Text | Tokenized PMC-VQA dataset with GPT-4o generated reasoning chains | 🤗 HF |
| MedVLThinker-pmc_vqa | Image-Text | Processed PMC-VQA dataset for medical visual question answering with RLVR | 🤗 HF |
| MedVLThinker-Eval | Image-Text | Comprehensive evaluation dataset for medical VQA benchmarks | 🤗 HF |
from datasets import load_dataset
# Load evaluation dataset
eval_dataset = load_dataset("UCSC-VLAA/MedVLThinker-Eval")
# Load training dataset with reasoning
train_dataset = load_dataset("UCSC-VLAA/MedVLThinker-pmc_vqa-gpt_4o_reasoning-tokenized")
# Load PMC-VQA dataset
pmc_dataset = load_dataset("UCSC-VLAA/MedVLThinker-pmc_vqa")
# Load Medical23k tokenized dataset
m23k_dataset = load_dataset("UCSC-VLAA/MedVLThinker-m23k-tokenized")
Our framework supports evaluation on the following medical VQA datasets:
All datasets follow a unified format:
{
"images": [PIL.Image], # List of images
"question": str, # Question text
"options": Dict[str, str], # Multiple choice options
"answer_label": str, # Correct answer label (A, B, C, D)
"answer": str, # Full answer text
"reasoning": str, # Chain-of-thought reasoning (optional)
"dataset_name": str, # Source dataset name
"dataset_index": int # Unique sample identifier
}
4 commits
2
stars
4
commits
2
linked in READMEs
Aug 15, 2025
updated
Code: https://github.com/UCSC-VLAA/MedVLThinker Project Page: https://ucsc-vlaa.github.io/MedVLThinker/
Our project provides several curated datasets for medical vision-language understanding and training:
| Dataset | Modality | Description | Download |
|---|---|---|---|
| MedVLThinker-m23k-tokenized | Text-only | Tokenized version of the m23k dataset | 🤗 HF |
| MedVLThinker-pmc_vqa-gpt_4o_reasoning-tokenized | Image-Text | Tokenized PMC-VQA dataset with GPT-4o generated reasoning chains | 🤗 HF |
| MedVLThinker-pmc_vqa | Image-Text | Processed PMC-VQA dataset for medical visual question answering with RLVR | 🤗 HF |
| MedVLThinker-Eval | Image-Text | Comprehensive evaluation dataset for medical VQA benchmarks | 🤗 HF |
from datasets import load_dataset
# Load evaluation dataset
eval_dataset = load_dataset("UCSC-VLAA/MedVLThinker-Eval")
# Load training dataset with reasoning
train_dataset = load_dataset("UCSC-VLAA/MedVLThinker-pmc_vqa-gpt_4o_reasoning-tokenized")
# Load PMC-VQA dataset
pmc_dataset = load_dataset("UCSC-VLAA/MedVLThinker-pmc_vqa")
# Load Medical23k tokenized dataset
m23k_dataset = load_dataset("UCSC-VLAA/MedVLThinker-m23k-tokenized")
Our framework supports evaluation on the following medical VQA datasets:
All datasets follow a unified format:
{
"images": [PIL.Image], # List of images
"question": str, # Question text
"options": Dict[str, str], # Multiple choice options
"answer_label": str, # Correct answer label (A, B, C, D)
"answer": str, # Full answer text
"reasoning": str, # Chain-of-thought reasoning (optional)
"dataset_name": str, # Source dataset name
"dataset_index": int # Unique sample identifier
}
4 commits