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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2021
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| Online-Zugang: | https://arxiv.org/abs/2110.14484 |
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| _version_ | 1866909782206578688 |
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| author | Mao, Kunpeng Li, Ruoyu Cheng, Junlong Huang, Danmei Song, Zhiping Liu, ZeKui |
| author_facet | Mao, Kunpeng Li, Ruoyu Cheng, Junlong Huang, Danmei Song, Zhiping Liu, ZeKui |
| contents | In recent years, deep convolutional neural network-based segmentation methods have achieved state-of-the-art performance for many medical analysis tasks. However, most of these approaches rely on optimizing the U-Net structure or adding new functional modules, which overlooks the complementation and fusion of coarse-grained and fine-grained semantic information. To address these issues, we propose a 2D medical image segmentation framework called Progressive Learning Network (PL-Net), which comprises Internal Progressive Learning (IPL) and External Progressive Learning (EPL). PL-Net offers the following advantages: (1) IPL divides feature extraction into two steps, allowing for the mixing of different size receptive fields and capturing semantic information from coarse to fine granularity without introducing additional parameters; (2) EPL divides the training process into two stages to optimize parameters and facilitate the fusion of coarse-grained information in the first stage and fine-grained information in the second stage. We conducted comprehensive evaluations of our proposed method on five medical image segmentation datasets, and the experimental results demonstrate that PL-Net achieves competitive segmentation performance. It is worth noting that PL-Net does not introduce any additional learnable parameters compared to other U-Net variants. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2110_14484 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | PL-Net: Progressive Learning Network for Medical Image Segmentation Mao, Kunpeng Li, Ruoyu Cheng, Junlong Huang, Danmei Song, Zhiping Liu, ZeKui Image and Video Processing Computer Vision and Pattern Recognition Machine Learning In recent years, deep convolutional neural network-based segmentation methods have achieved state-of-the-art performance for many medical analysis tasks. However, most of these approaches rely on optimizing the U-Net structure or adding new functional modules, which overlooks the complementation and fusion of coarse-grained and fine-grained semantic information. To address these issues, we propose a 2D medical image segmentation framework called Progressive Learning Network (PL-Net), which comprises Internal Progressive Learning (IPL) and External Progressive Learning (EPL). PL-Net offers the following advantages: (1) IPL divides feature extraction into two steps, allowing for the mixing of different size receptive fields and capturing semantic information from coarse to fine granularity without introducing additional parameters; (2) EPL divides the training process into two stages to optimize parameters and facilitate the fusion of coarse-grained information in the first stage and fine-grained information in the second stage. We conducted comprehensive evaluations of our proposed method on five medical image segmentation datasets, and the experimental results demonstrate that PL-Net achieves competitive segmentation performance. It is worth noting that PL-Net does not introduce any additional learnable parameters compared to other U-Net variants. |
| title | PL-Net: Progressive Learning Network for Medical Image Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2110.14484 |