Multi-level Asymmetric Contrastive Learning for Volumetric Medical Image Segmentation Pre-training
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arXiv
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| Format: | Preprint |
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2023
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| author | Zeng, Shuang Zhu, Lei Zhang, Xinliang Nnamdi, Micky C Shi, Wenqi Tamo, J Ben Chen, Qian He, Hangzhou Jin, Lujia Tian, Zifeng Ren, Qiushi Xie, Zhaoheng Lu, Yanye |
| author_facet | Zeng, Shuang Zhu, Lei Zhang, Xinliang Nnamdi, Micky C Shi, Wenqi Tamo, J Ben Chen, Qian He, Hangzhou Jin, Lujia Tian, Zifeng Ren, Qiushi Xie, Zhaoheng Lu, Yanye |
| contents | Medical image segmentation is a fundamental yet challenging task due to the arduous process of acquiring large volumes of high-quality labeled data from experts. Contrastive learning offers a promising but still problematic solution to this dilemma. Firstly existing medical contrastive learning strategies focus on extracting image-level representation, which ignores abundant multi-level representations. Furthermore they underutilize the decoder either by random initialization or separate pre-training from the encoder, thereby neglecting the potential collaboration between the encoder and decoder. To address these issues, we propose a novel multi-level asymmetric contrastive learning framework named MACL for volumetric medical image segmentation pre-training. Specifically, we design an asymmetric contrastive learning structure to pre-train encoder and decoder simultaneously to provide better initialization for segmentation models. Moreover, we develop a multi-level contrastive learning strategy that integrates correspondences across feature-level, image-level, and pixel-level representations to ensure the encoder and decoder capture comprehensive details from representations of varying scales and granularities during the pre-training phase. Finally, experiments on 8 medical image datasets indicate our MACL framework outperforms existing 11 contrastive learning strategies. i.e. Our MACL achieves a superior performance with more precise predictions from visualization figures and 1.72%, 7.87%, 2.49% and 1.48% Dice higher than previous best results on ACDC, MMWHS, HVSMR and CHAOS with 10% labeled data, respectively. And our MACL also has a strong generalization ability among 5 variant U-Net backbones. Our code will be released at https://github.com/stevezs315/MACL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_11876 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multi-level Asymmetric Contrastive Learning for Volumetric Medical Image Segmentation Pre-training Zeng, Shuang Zhu, Lei Zhang, Xinliang Nnamdi, Micky C Shi, Wenqi Tamo, J Ben Chen, Qian He, Hangzhou Jin, Lujia Tian, Zifeng Ren, Qiushi Xie, Zhaoheng Lu, Yanye Computer Vision and Pattern Recognition Artificial Intelligence Medical image segmentation is a fundamental yet challenging task due to the arduous process of acquiring large volumes of high-quality labeled data from experts. Contrastive learning offers a promising but still problematic solution to this dilemma. Firstly existing medical contrastive learning strategies focus on extracting image-level representation, which ignores abundant multi-level representations. Furthermore they underutilize the decoder either by random initialization or separate pre-training from the encoder, thereby neglecting the potential collaboration between the encoder and decoder. To address these issues, we propose a novel multi-level asymmetric contrastive learning framework named MACL for volumetric medical image segmentation pre-training. Specifically, we design an asymmetric contrastive learning structure to pre-train encoder and decoder simultaneously to provide better initialization for segmentation models. Moreover, we develop a multi-level contrastive learning strategy that integrates correspondences across feature-level, image-level, and pixel-level representations to ensure the encoder and decoder capture comprehensive details from representations of varying scales and granularities during the pre-training phase. Finally, experiments on 8 medical image datasets indicate our MACL framework outperforms existing 11 contrastive learning strategies. i.e. Our MACL achieves a superior performance with more precise predictions from visualization figures and 1.72%, 7.87%, 2.49% and 1.48% Dice higher than previous best results on ACDC, MMWHS, HVSMR and CHAOS with 10% labeled data, respectively. And our MACL also has a strong generalization ability among 5 variant U-Net backbones. Our code will be released at https://github.com/stevezs315/MACL. |
| title | Multi-level Asymmetric Contrastive Learning for Volumetric Medical Image Segmentation Pre-training |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2309.11876 |