Shuangchun Gui and Zhenkun Wang
This repository contains the pytorch implementation code and evaluation scripts.
conda env create -f environment.yaml
The code has been test on Linux operating system. It runs on both CPU and GPU. Equivalence of basic OS commands such as unzip, cd, wget, etc. will be needed to run in Windows or Mac OS.
Download CholecT45 dataset
Expand this to visualize the dataset directory structure.
──CholecT45
├───data
│ ├───VID01
│ │ ├───000000.png
│ │ ├───000001.png
│ │ ├───000002.png
│ │ ├───
│ │ └───N.png
│ ├───VID02
│ │ ├───000000.png
│ │ ├───000001.png
│ │ ├───000002.png
│ │ ├───
│ │ └───N.png
│ ├───
│ ├───
│ ├───
│ |
│ └───VIDN
│ ├───000000.png
│ ├───000001.png
│ ├───000002.png
│ ├───
│ └───N.png
|
├───triplet
│ ├───VID01.txt
│ ├───VID02.txt
│ ├───
│ └───VIDNN.txt
|
├───instrument
│ ├───VID01.txt
│ ├───VID02.txt
│ ├───
│ └───VIDNN.txt
|
├───verb
│ ├───VID01.txt
│ ├───VID02.txt
│ ├───
│ └───VIDNN.txt
|
├───target
│ ├───VID01.txt
│ ├───VID02.txt
│ ├───
│ └───VIDNN.txt
|
├───dict
│ ├───triplet.txt
│ ├───instrument.txt
│ ├───verb.txt
│ ├───target.txt
│ └───maps.txt
|
├───LICENSE
└───README.md
TERL's implementation is based on the code of RDV, Q2L, and SAHC. Thanks to them.
If this code is useful for your research, please consider citing:
@ARTICLE{111,
author={Gui, Shuangchun and Wang, Zhenkun},
title={Tail-Enhanced Representation Learning for Surgical Triplet Recognition},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={1--1},
year={2024},
organization={Springer}}
- Contact: Shuangchun Gui (12132667@mail.sustech.edu.cn)