arXiv Open Access 2022

GPU-Net: Lightweight U-Net with more diverse features

Heng Yu Di Fan Weihu Song

Lihat Sumber

Abstrak

Image segmentation is an important task in the medical image field and many convolutional neural networks (CNNs) based methods have been proposed, among which U-Net and its variants show promising performance. In this paper, we propose GP-module and GPU-Net based on U-Net, which can learn more diverse features by introducing Ghost module and atrous spatial pyramid pooling (ASPP). Our method achieves better performance with more than 4 times fewer parameters and 2 times fewer FLOPs, which provides a new potential direction for future research. Our plug-and-play module can also be applied to existing segmentation methods to further improve their performance.

Topik & Kata Kunci

eess.IV cs.CV

Penulis (3)

Heng Yu

Di Fan

Weihu Song

Format Sitasi

APA MLA BibTeX

Yu, H., Fan, D., Song, W. (2022). GPU-Net: Lightweight U-Net with more diverse features. https://arxiv.org/abs/2201.02656

Akses Cepat

Lihat di Sumber

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓