Skip to content

syan-dev/blazeneo

Repository files navigation

BlazeNeo: Blazing fast polyp segmentation and neoplasm detection

This repository provides code for paper "BlazeNeo: Blazing fast polyp segmentation and neoplasm detection" in IEEE Access, vol. 10, pp. 43669-43684, 2022.

Features

  • Real-time Speed: Our method, name BlazeNeo, can efficiently perform polyp segmentation and neoplasm detection from polyp videos with real-time speed (~81.5 FPS) on a single NVIDIA Tesla V100 GPU.
  • Cutting-edge Performance: Extensive experiments on the newly collected NeoPolyp dataset and comparisons to existing models. Moreover, we measure model latency and throughput on dedicated hardware in a setting similar to a real-life deployment.

Framework overview

Tutorials

Installation

Download our public dataset BKAI-IGH NeoPolyp-Small for your own experiments.

pip install -r requirements.txt

Model training

python train.py

Model evaluation

python eval.py

Prediction

python infer.py

Quantitative comparison

Performance metrics on the NeoPolyp-Clean test set for ColonSegNet, U-Net, DDANet, DoubleU-Net, PraNet, HardNet-MSEG, NeoUNet and BlazeNeo.

Method Diceseg Dice non Dice neo FPS Parameters GFLOPs
ColonSegNet 0.738 0.505 0.732 44.9 5,010,000 64,84
U-Net 0.785 0.525 0.773 69.6 31,043,651 103,59
DDANet 0.813 0.578 0.802 46.2 6,840,000 31,45
DoubleU-Net 0.837 0.621 0.832 43.2 18,836,804 83,62
HarDNet-MSEG 0.883 0.659 0.869 77.1 17,424,031 11,38
PraNet 0.895 0.705 0.873 55.6 30,501,341 13,11
NeoUNet 0.911 0.720 0.889 68.3 38,288,397 39,88
BlazeNeo (Ours) 0.904 0.717 0.885 81.5 17,143,324 11.06

Qualitative comparison

Qualitative comparison of the proposed method with other baseline methods: (a) image, (b) ground truth, (c) BlazeNeo (Ours), (d) NeoUNet, (e) PraNet, (f) HarDNet-MSEG, (g) UNet, (h) Double U-Net, (i) DDANet, and (j) ColonSegNet.

Ackowledgement

This work was funded by Vingroup Innovation Foundation (VINIF) under project code VINIF.2020.DA17. We thank IGH for collecting and annotating the data.

Cite

Please cite our work if you find it useful:

@ARTICLE{blazeneo2022,
  author={An, Nguyen S. and Lan, Phan N. and Hang, Dao V. and Long, Dao V. and Trung, Tran Q. and Thuy, Nguyen T. and Sang, Dinh V.},
  journal={IEEE Access}, 
  title={BlazeNeo: Blazing Fast Polyp Segmentation and Neoplasm Detection}, 
  year={2022},
  volume={10},
  pages={43669-43684}
}

FAQ

If you want to improve the usability or any piece of advice, please feel free to contact me directly ([email protected])

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages