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

Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation

February 1, 2023

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Authors

Huifeng Yao

The Hong Kong University of Science and Technology


Xiaowei Hu

The Chinese University of Hong Kong


Xiaomeng Li

The Hong Kong University of Science and Technology The Hong Kong University of Science and Technology Shenzhen Research Institute


DOI:

10.1609/aaai.v36i3.20217


Abstract:

Generalizing the medical image segmentation algorithms to unseen domains is an important research topic for computer-aided diagnosis and surgery. Most existing methods require a fully labeled dataset in each source domain. Although some researchers developed a semi-supervised domain generalized method, it still requires the domain labels. This paper presents a novel confidence-aware cross pseudo supervision algorithm for semi-supervised domain generalized medical image segmentation. The main goal is to enhance the pseudo label quality for unlabeled images from unknown distributions. To achieve it, we perform the Fourier transformation to learn low-level statistic information across domains and augment the images to incorporate cross-domain information. With these augmentations as perturbations, we feed the input to a confidence-aware cross pseudo supervision network to measure the variance of pseudo labels and regularize the network to learn with more confident pseudo labels. Our method sets new records on public datasets, i.e., M&Ms and SCGM. Notably, without using domain labels, our method surpasses the prior art that even uses domain labels by 11.67% on Dice on M&Ms dataset with 2% labeled data. Code is available at https://github.com/XMed-Lab/EPL SemiDG.

Topics: AAAI

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HOW TO CITE:

Huifeng Yao||Xiaowei Hu||Xiaomeng Li Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation Proceedings of the AAAI Conference on Artificial Intelligence (2022) 3099-3107.

Huifeng Yao||Xiaowei Hu||Xiaomeng Li Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation AAAI 2022, 3099-3107.

Huifeng Yao||Xiaowei Hu||Xiaomeng Li (2022). Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 3099-3107.

Huifeng Yao||Xiaowei Hu||Xiaomeng Li. Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.3099-3107.

Huifeng Yao||Xiaowei Hu||Xiaomeng Li. 2022. Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation. "Proceedings of the AAAI Conference on Artificial Intelligence". 3099-3107.

Huifeng Yao||Xiaowei Hu||Xiaomeng Li. (2022) "Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation", Proceedings of the AAAI Conference on Artificial Intelligence, p.3099-3107

Huifeng Yao||Xiaowei Hu||Xiaomeng Li, "Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation", AAAI, p.3099-3107, 2022.

Huifeng Yao||Xiaowei Hu||Xiaomeng Li. "Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.3099-3107.

Huifeng Yao||Xiaowei Hu||Xiaomeng Li. "Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 3099-3107.

Huifeng Yao||Xiaowei Hu||Xiaomeng Li. Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation. AAAI[Internet]. 2022[cited 2023]; 3099-3107.


ISSN: 2374-3468


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