TeCNO performs hierarchical prediction refinement with causal, dilated convolutions for surgical phase recognition and outperforms various state-of-the-art LSTM approaches!
Link to paper: TeCNO Paper
Follow these steps to get the code running on your local machine!
pip install -r requirements.txt
We are using the publicly available Cholec80 dataset. For training we split the videos into individual frames.
Run:
python train.py -c modules/cnn/config/config_feature_extract.yml
This will train your feature extractor and in the Test Step it will extract for each Video the features of all images and save it as .pkl
python train.py -c modules/mstcn/config/config_tcn.yml
@inproceedings{czempiel2020,
author = {Tobias Czempiel and
Magdalini Paschali and
Matthias Keicher and
Walter Simson and
Hubertus Feussner and
Seong Tae Kim and
Nassir Navab},
title = {TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional
Networks},
booktitle = {Medical Image Computing and Computer Assisted Intervention - {MICCAI}
2020 - 23nd International Conference, Shenzhen, China, October 4-8,
2020, Proceedings, Part {III}},
series = {Lecture Notes in Computer Science},
volume = {12263},
pages = {343--352},
publisher = {Springer},
year = {2020},
}
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