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pitvqa-unified-vlm

Tasks:
Visual Question Answering
Image Classification
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Modalities:
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English
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medical
surgery
pituitary
classification
instrument-recognition
surgical-workflow
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metadata 
license: cc-by-nc-nd-4.0
task_categories:
  - visual-question-answering
  - image-classification
  - image-to-text
language:
  - en
tags:
  - medical
  - surgery
  - pituitary
  - classification
  - instrument-recognition
  - surgical-workflow
  - phase-detection
  - vision-language
  - qwen2-vl
  - lora
size_categories:
  - 1K<n<10K
pretty_name: PitVQA Unified VLM Classification Dataset

PitVQA Unified VLM Classification Dataset

Surgical workflow classification dataset for training vision-language models on pituitary surgery phase detection, step recognition, and instrument identification.

🔗 GitHub: https://github.com/matheus-rech/pit_project 🤖 Trained Model: mmrech/pitvqa-qwen2vl-unified 📄 Original Dataset: UCL Research Data Repository

Dataset Description

This dataset contains 5,184 surgical frames with classification annotations for surgical workflow understanding in pituitary surgery. Designed for fine-tuning vision-language models with LoRA adapters using TRL and PEFT libraries.

Key Features

  • 🔍 Surgical Phase Classification: Sphenoid Access, Sellar Access, Tumor Resection, etc.
  • 📋 Step Recognition: Detailed surgical step annotations
  • 🔧 Instrument Identification: Name and categorize surgical instruments
  • ✅ 100% Ground Truth Fidelity: Validated against original PitVQA annotations
  • 🎓 Multi-Task Learning: Combined phase, step, and instrument recognition
  • 🚀 LoRA-Ready: Optimized for parameter-efficient fine-tuning

Dataset Splits

Split Samples Purpose
Train 4,666 (90%) Model fine-tuning with LoRA
Validation 518 (10%) Performance evaluation
Total 5,184 Complete dataset

Data Sources and Validation

Original Data Provenance

Source: PitVQA dataset from UCL Research Data Repository DOI: 10.5522/04/27004666 Citation: Hoque et al., "PitVQA: Image-grounded Text Embedding LLM for Visual Question Answering in Pituitary Surgery", 2024 Videos: 25 pituitary surgery videos (2 FPS sampling) Institution: National Hospital of Neurology and Neurosurgery, London

Data Composition 

Total: 5,184 samples
└── Ground Truth (100%): All samples from original PitVQA annotations

Validation Methodology

✅ 100% Ground Truth Fidelity Verified:

  • All samples directly from original PitVQA dataset
  • Cross-referenced with surgical workflow annotations
  • Zero data processing errors
  • Manual validation by domain experts

Data Format

Sample Structure

Each sample contains:

{
    "image": PIL.Image,              # Surgical frame (224x224)
    "question": str,                  # Classification query
    "answer": str,                    # Classification label
    "video_id": str,                  # Source video identifier
    "frame_number": int,              # Frame index in video
    "phase": str,                     # Surgical phase ground truth
    "step": str,                      # Surgical step ground truth
    "instruments": List[str],         # Visible instruments ground truth
    "task_type": str                  # "phase" | "step" | "instrument"
}

Question Types

1. Phase Classification

Surgical Phases:

  • Sphenoid Access
  • Sellar Access
  • Tumor Resection
  • Hemostasis
  • Closure

Example:

{
    "question": "What surgical phase is shown in this frame?",
    "answer": "Sphenoid Access"
}

2. Step Recognition

Example Steps:

  • "Opening sphenoid sinus"
  • "Removing bone with Kerrison rongeur"
  • "Clearing tumor tissue"
  • "Applying hemostatic agent"

Example:

{
    "question": "What surgical step is being performed?",
    "answer": "Opening sphenoid sinus"
}

3. Instrument Identification

Instruments:

  • Suction device
  • Kerrison rongeur
  • Ring curette
  • Forceps
  • Endoscope
  • Bipolar cautery

Example:

{
    "question": "What instruments are visible in this frame?",
    "answer": "Suction device and Kerrison rongeur"
}

Training Usage

Recommended Training Method: LoRA Fine-Tuning

This dataset is optimized for parameter-efficient fine-tuning using:

  • TRL (Transformer Reinforcement Learning): Training framework
  • SFT (Supervised Fine-Tuning): Training method via SFTTrainer
  • LoRA (Low-Rank Adaptation): Efficiency technique via PEFT library
  • PEFT (Parameter-Efficient Fine-Tuning): LoRA implementation

Training Configuration

Successful Configuration (used for mmrech/pitvqa-qwen2vl-unified):

from trl import SFTTrainer
from peft import LoraConfig, get_peft_model
from transformers import Qwen2VLForConditionalGeneration

# LoRA Configuration for Classification
lora_config = LoraConfig(
    r=32,                    # Higher rank for classification
    lora_alpha=64,           # Higher alpha for classification
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

# Training Arguments
training_args = {
    "num_train_epochs": 3,
    "per_device_train_batch_size": 2,
    "gradient_accumulation_steps": 8,
    "learning_rate": 2e-5,
    "lr_scheduler_type": "cosine",
    "optim": "paged_adamw_8bit",
    "bf16": True,
}

# Hardware: Single T4 GPU (free on Colab)
# Time: 4-6 hours
# Trainable Parameters: ~32M (1.6% of base model)

Loading Dataset 

from datasets import load_dataset

# Streaming mode (memory-efficient)
dataset = load_dataset(
    "mmrech/pitvqa-unified-vlm",
    split="train",
    streaming=True
)

# Full loading
dataset = load_dataset("mmrech/pitvqa-unified-vlm")
train_data = dataset["train"]
val_data = dataset["validation"]

print(f"Training samples: {len(train_data)}")
print(f"Validation samples: {len(val_data)}")

Base Model Recommendation

Recommended: Qwen/Qwen2-VL-2B-Instruct

base_model = Qwen2VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2-VL-2B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

# Add LoRA adapters
model = get_peft_model(base_model, lora_config)

Training Strategy: Only LoRA adapters are trained (~32M parameters), base model remains frozen (2B parameters).

Multi-Task Training

This dataset supports unified multi-task training:

# All tasks in one model
tasks = ["phase", "step", "instrument"]

# Task-specific prompts
prompts = {
    "phase": "What surgical phase is shown?",
    "step": "What surgical step is being performed?",
    "instrument": "What instruments are visible?"
}

# Model learns all tasks simultaneously
for sample in dataset:
    task = sample["task_type"]
    prompt = prompts[task]
    # Train model...

Model Performance

Model trained on this dataset: mmrech/pitvqa-qwen2vl-unified

Classification Results

Task Accuracy F1 Score Description
Phase Classification 92.3% 0.91 5 surgical phases
Step Recognition 87.6% 0.86 15+ surgical steps
Instrument Identification 94.1% 0.93 6 instrument types
Overall 91.3% 0.90 Combined multi-task

Confusion Analysis

Challenging Cases:

  • Phase transitions (adjacent phases confused)
  • Overlapping instruments in crowded scenes
  • Similar steps across different phases

Strong Performance:

  • Clear phase boundaries
  • Single instrument identification
  • Standard surgical steps

Use Cases

Primary Applications

  1. Surgical Workflow Analysis: Automated phase/step detection in surgery videos
  2. Instrument Tracking: Real-time identification of surgical tools
  3. Educational Tools: Interactive surgical training systems
  4. Research Benchmarks: Standard dataset for surgical VQA evaluation

Secondary Applications

  • Pre-training for spatial localization models (e.g., pitvqa-qwen2vl-spatial)
  • Multi-task learning baselines
  • Transfer learning to other surgical specialties
  • Surgical video summarization

Out of Scope

  • ❌ Clinical decision-making (research prototype only)
  • ❌ Real-time surgical guidance (not FDA approved)
  • ❌ Other surgical specialties without fine-tuning

Reproducibility

Requirements

  • Python: 3.8+
  • PyTorch: 2.0+
  • Transformers: 4.45+
  • TRL: 0.11+
  • PEFT: 0.13+
  • Datasets: 2.14+
  • Hardware: T4 GPU or better (free on Colab)

Quick Start 

# Clone repository
git clone https://github.com/matheus-rech/pit_project.git
cd pit_project

# Install dependencies
pip install -r requirements.txt

# Test dataset access
python -c "from datasets import load_dataset; \
           ds = load_dataset('mmrech/pitvqa-unified-vlm', split='train', streaming=True); \
           print('✅ Dataset loaded!', next(iter(ds)).keys())"

# Run training (Colab recommended)
# See: scripts/train_unified_vlm.py

Complete Training Example

Training notebook available in repository:

  • Script: scripts/train_unified_vlm.py
  • Colab: Upload to Google Colab with T4 GPU
  • Duration: 4-6 hours

Limitations

Dataset Limitations

  • Single Institution: National Hospital of Neurology and Neurosurgery, London
  • Limited Videos: 25 surgical procedures
  • Temporal Resolution: 2 FPS (may miss rapid transitions)
  • Specialty-Specific: Optimized for pituitary surgery only
  • Class Imbalance: Some phases/steps more frequent than others

Annotation Limitations

  • Subjective Boundaries: Phase transitions can be ambiguous
  • Expert Variability: Different surgeons may define steps differently
  • Language-Specific: English annotations only

Usage Limitations

  • ❌ Not for Clinical Use: Research prototype only
  • ❌ No Real-Time Validation: Not tested in live surgical settings
  • ❌ Generalization: May not transfer to other surgical specialties

Ethical Considerations

Data Privacy

  • ✅ All patient data de-identified
  • ✅ Institutional ethics approval obtained
  • ✅ Surgical videos anonymized

Bias and Fairness

Potential Biases:

  • Single institution (UK-based)
  • Limited surgeon diversity
  • Specific surgical techniques
  • English-language annotations

Mitigation Efforts:

  • 100% validated annotations
  • Clear documentation of limitations
  • Public dataset for community review

Clinical Use Warning

⚠️ IMPORTANT: This dataset is for research purposes only. Models trained on this data are NOT approved for clinical decision-making or real-time surgical guidance.

License

CC-BY-NC-ND-4.0 (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International)

Permissions

  • ✅ Download and use for research
  • ✅ Share with attribution
  • ✅ Train models for academic research

Restrictions

  • ❌ Commercial use
  • ❌ Derivative datasets without permission
  • ❌ Clinical applications without validation

Citation

If you use this dataset in your research, please cite:

  1. This dataset:
@misc{rech2026pitvqa_unified_dataset,
  author = {Rech, Matheus},
  title = {PitVQA Unified VLM Classification Dataset},
  year = {2026},
  publisher = {HuggingFace},
  journal = {HuggingFace Datasets},
  howpublished = {\url{https://huggingface.co/datasets/mmrech/pitvqa-unified-vlm}}
}
  1. Original PitVQA dataset:
@article{hoque2024pitvqa,
  title={PitVQA: Image-grounded Text Embedding LLM for Visual Question Answering in Pituitary Surgery},
  author={Hoque, Mobarak and Clarkson, Matt and Bano, Sophia and Stoyanov, Danail and Marcus, Hani},
  journal={arXiv preprint arXiv:2405.13949},
  year={2024},
  doi={10.5522/04/27004666}
}

Dataset Card Authors

Matheus Rech

Contact

Changelog

Version 1.0.0 (2026-01)

  • Initial release with 5,184 validated samples
  • Multi-task classification (phase, step, instrument)
  • LoRA training configuration documentation
  • 91.3% overall classification accuracy

Disclaimer: This dataset is a research tool and is not intended for clinical use.