YONA: You Only Need One Adjacent Reference-frame for Accurate and Fast Video Polyp Detection

by Yuncheng Jiang, Zixun Zhang, Ruimao Zhang, Guanbin Li, Shuguang Cui, and Zhen Li

Introduction

Accurate polyp detection is essential for assisting clinical rectal cancer diagnoses. Colonoscopy videos contain richer information than still images, making them a valuable resource for deep learning methods. However, unlike common fixed-camera video, the camera-moving scene in colonoscopy videos can cause rapid video jitters, leading to unstable training for existing video detection models. In this paper, we propose the YONA (You Only Need one Adjacent Reference-frame) method, an efficient end-to-end training framework for video polyp detection. YONA fully exploits the information of one previous adjacent frame and conducts polyp detection on the current frame without multi-frame collaborations. Specifically, for the foreground, YONA adaptively aligns the current frame’s channel activation patterns with its adjacent refer- ence frames according to their foreground similarity. For the background, YONA conducts background dynamic alignment guided by inter-frame difference to eliminate the invalid features produced by drastic spatial jitters. Moreover, YONA applies cross-frame contrastive learning dur- ing training, leveraging the ground truth bounding box to improve the model’s perception of polyp and background. Quantitative and quali- tative experiments on three public challenging benchmarks demonstrate that our proposed YONA outperforms previous state-of-the-art competi- tors by a large margin in both accuracy and speed.

Clone repository

git clone https://github.com/yuncheng97/YONA.git
cd YONA/

File tree

YONA                         
├── README.md
├── data.py
├── figures
│   └── framework.png
├── model.py
├── pretrained
├── result
├── pvtv2.py
├── resnet.py
├── train.py
├── train.sh
└── utils.py

Download dataset

The training and testing datasets come from public colonoscopy benchmarks: 'SUNSEG', 'LDPolypVideo', 'CVC-VideoClinicDB' and 'PolypGen'. Download these datasets and unzip them into data folder.

• SUNSEG
• LDPolypVideo
• CVC-VideoClinicDB (you need contact the author to get the access permission)

Prerequisites

• Python 3
• Pytorch 1.11
• OpenCV 4.7
• Numpy 1.23
• Albumentations 1.3.1

Pretrained

download the pretrained backbone models and put them in the pretrained folder

• PVT: Baidu:bhrf
• ResNet50: Baidu:hc2h
• ResNet101: Baidu:34an

Training

Set your own configuration before training. Make sure you set the right dataset path in '--data_path' and save path in '--save_path'.

    python train.py

    or

    bash train.sh

Citation

• If you find this work is helpful, please cite our paper

@article{jiang2023yona,
  title={YONA: You Only Need One Adjacent Reference-frame for Accurate and Fast Video Polyp Detection},
  author={Jiang, Yuncheng and Zhang, Zixun and Zhang, Ruimao and Li, Guanbin and Cui, Shuguang and Li, Zhen},
  journal={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023},
  year={2023}
}