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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 3: AAAI-22 Technical Tracks 3

Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation

February 1, 2023

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Authors

Jiacheng Wang

Xiamen University


Xiaomeng Li

The Hong Kong University of Science and Technology


Yiming Han

Peking University


Jing Qin

The Hong Kong Polytechnic University


Liansheng Wang

Xiamen University


Zhou Qichao

Manteia


DOI:

10.1609/aaai.v36i3.20146


Abstract:

Automatic delineation of organ-at-risk (OAR) and gross-tumor-volume (GTV) is of great significance for radiotherapy planning. However, it is a challenging task to learn powerful representations for accurate delineation under limited pixel (voxel)-wise annotations. Contrastive learning at pixel-level can alleviate the dependency on annotations by learning dense representations from unlabeled data. Recent studies in this direction design various contrastive losses on the feature maps, to yield discriminative features for each pixel in the map. However, pixels in the same map inevitably share semantics to be closer than they actually are, which may affect the discrimination of pixels in the same map and lead to the unfair comparison to pixels in other maps. To address these issues, we propose a separated region-level contrastive learning scheme, namely SepaReg, the core of which is to separate each image into regions and encode each region separately. Specifically, SepaReg comprises two components: a structure-aware image separation (SIS) module and an intra- and inter-organ distillation (IID) module. The SIS is proposed to operate on the image set to rebuild a region set under the guidance of structural information. The inter-organ representation will be learned from this set via typical contrastive losses cross regions. On the other hand, the IID is proposed to tackle the quantity imbalance in the region set as tiny organs may produce fewer regions, by exploiting intra-organ representations. We conducted extensive experiments to evaluate the proposed model on a public dataset and two private datasets. The experimental results demonstrate the effectiveness of the proposed model, consistently achieving better performance than state-of-the-art approaches. Code is available at https://github.com/jcwang123/Separate_CL.

Topics: AAAI

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HOW TO CITE:

Jiacheng Wang||Xiaomeng Li||Yiming Han||Jing Qin||Liansheng Wang||Zhou Qichao Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation Proceedings of the AAAI Conference on Artificial Intelligence (2022) 2459-2467.

Jiacheng Wang||Xiaomeng Li||Yiming Han||Jing Qin||Liansheng Wang||Zhou Qichao Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation AAAI 2022, 2459-2467.

Jiacheng Wang||Xiaomeng Li||Yiming Han||Jing Qin||Liansheng Wang||Zhou Qichao (2022). Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation. Proceedings of the AAAI Conference on Artificial Intelligence, 2459-2467.

Jiacheng Wang||Xiaomeng Li||Yiming Han||Jing Qin||Liansheng Wang||Zhou Qichao. Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.2459-2467.

Jiacheng Wang||Xiaomeng Li||Yiming Han||Jing Qin||Liansheng Wang||Zhou Qichao. 2022. Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation. "Proceedings of the AAAI Conference on Artificial Intelligence". 2459-2467.

Jiacheng Wang||Xiaomeng Li||Yiming Han||Jing Qin||Liansheng Wang||Zhou Qichao. (2022) "Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation", Proceedings of the AAAI Conference on Artificial Intelligence, p.2459-2467

Jiacheng Wang||Xiaomeng Li||Yiming Han||Jing Qin||Liansheng Wang||Zhou Qichao, "Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation", AAAI, p.2459-2467, 2022.

Jiacheng Wang||Xiaomeng Li||Yiming Han||Jing Qin||Liansheng Wang||Zhou Qichao. "Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.2459-2467.

Jiacheng Wang||Xiaomeng Li||Yiming Han||Jing Qin||Liansheng Wang||Zhou Qichao. "Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 2459-2467.

Jiacheng Wang||Xiaomeng Li||Yiming Han||Jing Qin||Liansheng Wang||Zhou Qichao. Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation. AAAI[Internet]. 2022[cited 2023]; 2459-2467.


ISSN: 2374-3468


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