Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures

Kun Yuan, Vinkle Srivastav, Tong Yu, Joel L. Lavanchy, Pietro Mascagni, Nassir Navab, Nicolas Padoy

SurgVLP (Surgical Vision Language Pre-training) is a neural network pretrained on large-scale (image, text) pairs from surgical video lectures. It uses automatic speech recognition to generate text transcriptions, addressing the unique linguistic challenges of surgical language and creating an SVL (Surgical Vision-Language Pretraining) dataset. SurgVLP aligns video clip embeddings with corresponding text embeddings in a joint latent space through a contrastive learning objective. Without manual annotations, SurgVLP excels in vision-language tasks like text-based video retrieval, temporal activity grounding, and video captioning. It also demonstrates zero-shot applicability to conventional surgical computer vision tasks, such as phase recognition, without task-specific fine-tuning.

In this repo we provide:

SurgVLP weights trained on the SVL dataset.
Evaluation code for zero-shot recognition on surgical phases on the Cholec-80 dataset.

Surgical Vision-Language Pretraining Dataset (SVL)

SVL

Surgical Vision Language Pre-training (SurgVLP)

SVL

Usage

First, create a anaconda environment and install PyTorch (or later) and torchvision, as well as small additional dependencies, and then install this repo as a Python package. On a CUDA GPU machine, the following will do the trick:

$ pip install git+https://github.com/openai/CLIP.git
$ pip install git+https://github.com/CAMMA-public/SurgVLP.git

import torch
import surgvlp
from PIL import Image
from mmengine.config import Config
device = "cuda" if torch.cuda.is_available() else "cpu"

configs = Config.fromfile('./tests/config.py')['config']
model, preprocess = surgvlp.load(configs.model_config, device=device)

image = preprocess(Image.open("./tests/SurgVLP.png")).unsqueeze(0).to(device)
text = surgvlp.tokenize(['This is preparation phase', 'This is clipcutting phase'], device=device)

with torch.no_grad():
    output_dict = model(image, text , mode='all')

    image_embeddings = output_dict['img_emb']
    text_embeddings= output_dict['text_emb']

    image_embeddings /= image_embeddings.norm(dim=-1, keepdim=True)
    text_embeddings /= text_embeddings.norm(dim=-1, keepdim=True)

    logits_per_image = 100.0 * image_embeddings @ text_embeddings.T
    probs = logits_per_image.softmax(dim=-1).cpu().numpy()

print("Label probs:", probs)

API

The SurgVLP module surgvlp provides the following methods:

`surgvlp.available_models()`

Returns the names of the available pre-trained surgical vision-language models.

`surgvlp.load(config, device, download_root)`

Returns the model and the TorchVision transform needed by the model, specified by the model name returned by surgvlp.available_models(). It will download the model as necessary. The name argument can also be a path to a local checkpoint.

The device to run the model can be optionally specified, and the default is to use the first CUDA device if there is any, otherwise the CPU.

`surgvlp.load_dataset(config)`

Returns a torch dataset object given dataset config

`surgvlp.tokenize(text: Union[str, List[str]], padding, max_length, truncation, model_name, device)`

Returns a dictionary containing tokenized sequences of given text input(s). This can be used as the input to the model.

The model returned by surgvlp.load() supports the following methods:

`model(image: Tensor, text: None, mode='video')`

Given a batch of images, returns the image features encoded by the vision portion of the SurgVLP model.

`model(image: None, text: Tensor, mode='text)`

Given a batch of text tokens, returns the text features encoded by the language portion of the SurgVLP model.

`model(image: Tensor, text: Tensor, mode='all')`

Given a batch of images and a batch of text tokens, returns the image and textual embeddings.

More Examples

Zero-Shot Surgical Phase Recognition

The code below performs zero-shot phase recognition using SurgVLP. This example takes an image from the Cholec80 dataset testing set, and predicts the most likely phase labels from the dataset. To start with, you need to download the Cholec80 dataset from the offical website and extract frames. We recommend you to download our processed frames and csv files.

Step 1

Download cholec80 testing set from our S3 server and unzip it into ./tests folder:

$ wget --content-disposition https://seafile.unistra.fr/f/11a4f6309d8b428f8357/?dl=1
$ wget --content-disposition https://seafile.unistra.fr/f/7d29ecf9ff9d4bad8a0f/?dl=1
$ unzip csvs.zip -d ./tests/
$ unzip cholec80_test_frames.zip -d ./tests/

Step 2

Edit the configuration file located at ./tests/config.py with the your own values:

csv_root='./csvs' # replace with your own value,
video_root='./tmp/' # replace with your own value,

Step 3

Run the tests/zero_shot.py to conduct zero-shot surgical phase recognition of cholec80 testing set:

$ cd tests
$ python zero_shot.py --save_plot=False --class_prompt=./class_prompt.txt --config --config=./config.py --batch_size=400

Citing SurgVLP

Please cite this work if you find it interesting and useful:

@article{yuan2023learning,
  title={Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures},
  author={Yuan, Kun and Srivastav, Vinkle and Yu, Tong and Lavanchy, Joel and Mascagni, Pietro and Navab, Nassir and Padoy, Nicolas},
  year={2023},
  eprint={2307.15220},
  archivePrefix={arXiv}
}

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License

The code and the models are available for non-commercial scientific research purposes as defined in the CC BY-NC-SA 4.0. By downloading and using this code you agree to the terms in the LICENSE. Third-party codes are subject to their respective licenses.

By downloading and using this repo, you agree to these terms and conditions.

Acknowledgement

This work has received funding from the European Union (ERC, CompSURG, 101088553). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. This work was also partially supported by French state funds managed by the ANR under Grants ANR-20-CHIA-0029-01 and ANR-10-IAHU-02.

CAMMA-public / SurgVLP

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