MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation

TL;DR

MiniSeg is a lightweight, efficient deep learning model with only 83K parameters designed for COVID-19 CT segmentation, enabling fast training, deployment, and adaptation with limited data.

Contribution

It introduces MiniSeg, a novel extremely minimal network for COVID-19 segmentation, addressing overfitting and computational efficiency issues in existing methods.

Findings

MiniSeg achieves high efficiency with only 83K parameters.

MiniSeg demonstrates strong segmentation performance on COVID-19 data.

The benchmark facilitates comparison of MiniSeg with traditional methods.

Abstract

The rapid spread of the new pandemic, i.e., COVID-19, has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected CT area segmentation, has attracted much attention. However, the publicly available COVID-19 training data are limited, easily causing overfitting for traditional deep learning methods that are usually data-hungry with millions of parameters. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional deep learning methods are usually computationally intensive. To address the above problems, we propose MiniSeg, a lightweight deep learning model for efficient COVID-19 segmentation. Compared with traditional segmentation methods, MiniSeg has several significant strengths: i) it only has 83K parameters and is thus not easy to overfit; ii) it has high computational efficiency and is thus convenient for practical deployment; iii) it can be fast retrained by other users using their private COVID-19 data for further improving performance. In addition, we build a comprehensive COVID-19 segmentation benchmark for comparing MiniSeg to traditional methods.