HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS

TL;DR

HarDNet-MSEG is a lightweight, high-speed neural network for polyp segmentation that achieves state-of-the-art accuracy with over 0.9 mean Dice and 86 FPS on standard datasets.

Contribution

It introduces a simple encoder-decoder architecture combining HarDNet backbone with a cascaded partial decoder for efficient polyp segmentation.

Findings

Achieves 0.904 mean Dice on Kvasir-SEG

Runs at 86.7 FPS on a GeForce RTX 2080 Ti

Outperforms previous methods in accuracy and speed

Abstract

We propose a new convolution neural network called HarDNet-MSEG for polyp segmentation. It achieves SOTA in both accuracy and inference speed on five popular datasets. For Kvasir-SEG, HarDNet-MSEG delivers 0.904 mean Dice running at 86.7 FPS on a GeForce RTX 2080 Ti GPU. It consists of a backbone and a decoder. The backbone is a low memory traffic CNN called HarDNet68, which has been successfully applied to various CV tasks including image classification, object detection, multi-object tracking and semantic segmentation, etc. The decoder part is inspired by the Cascaded Partial Decoder, known for fast and accurate salient object detection. We have evaluated HarDNet-MSEG using those five popular datasets. The code and all experiment details are available at Github. https://github.com/james128333/HarDNet-MSEG