ResGANet: Residual group attention network for medical image classification and segmentation
Abstrak
In recent years, deep learning technology has shown superior performance in different fields of medical image analysis. Some deep learning architectures have been proposed and used for computational pathology classification, segmentation, and detection tasks. Due to their simple, modular structure, most downstream applications still use ResNet and its variants as the backbone network. This paper proposes a modular group attention block that can capture feature dependencies in medical images in two independent dimensions: channel and space. By stacking these group attention blocks in ResNet-style, we obtain a new ResNet variant called ResGANet. The stacked ResGANet architecture has 1.51-3.47 times fewer parameters than the original ResNet and can be directly used for downstream medical image segmentation tasks. Many experiments show that the proposed ResGANet is superior to state-of-the-art backbone models in medical image classification tasks. Applying it to different segmentation networks can improve the baseline model in medical image segmentation tasks without changing the network architecture. We hope that this work provides a promising method for enhancing the feature representation of convolutional neural networks (CNNs) in the future.
Topik & Kata Kunci
Penulis (9)
Junlong Cheng
Sheng Tian
Long Yu
Chengrui Gao
Xiaojing Kang
Xiang Ma
Weidong Wu
Shi-Jian Liu
Hongchun Lu
Akses Cepat
PDF tidak tersedia langsung
Cek di sumber asli →- Tahun Terbit
- 2021
- Bahasa
- en
- Total Sitasi
- 201×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1016/j.media.2021.102313
- Akses
- Open Access ✓