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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

February 1, 2023

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Authors

Hao Zheng

University of Notre Dame


Yizhe Zhang

University of Notre Dame


Lin Yang

University of Notre Dame


Peixian Liang

University of Notre Dame


Zhuo Zhao

University of Notre Dame


Chaoli Wang

University of Notre Dame


Danny Z. Chen

University of Notre Dame


DOI:

10.1609/aaai.v33i01.33015909


Abstract:

3D image segmentation plays an important role in biomedical image analysis. Many 2D and 3D deep learning models have achieved state-of-the-art segmentation performance on 3D biomedical image datasets. Yet, 2D and 3D models have their own strengths and weaknesses, and by unifying them together, one may be able to achieve more accurate results. In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models. First, we develop a fully convolutional network based meta-learner to learn how to improve the results from 2D and 3D models (base-learners). Then, to minimize over-fitting for our sophisticated meta-learner, we devise a new training method that uses the results of the baselearners as multiple versions of “ground truths”. Furthermore, since our new meta-learner training scheme does not depend on manual annotation, it can utilize abundant unlabeled 3D image data to further improve the model. Extensive experiments on two public datasets (the HVSMR 2016 Challenge dataset and the mouse piriform cortex dataset) show that our approach is effective under fully-supervised, semisupervised, and transductive settings, and attains superior performance over state-of-the-art image segmentation methods.

Topics: AAAI

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HOW TO CITE:

Hao Zheng||Yizhe Zhang||Lin Yang||Peixian Liang||Zhuo Zhao||Chaoli Wang||Danny Z. Chen A New Ensemble Learning Framework for 3D Biomedical Image Segmentation Proceedings of the AAAI Conference on Artificial Intelligence (2019) 5909-5916.

Hao Zheng||Yizhe Zhang||Lin Yang||Peixian Liang||Zhuo Zhao||Chaoli Wang||Danny Z. Chen A New Ensemble Learning Framework for 3D Biomedical Image Segmentation AAAI 2019, 5909-5916.

Hao Zheng||Yizhe Zhang||Lin Yang||Peixian Liang||Zhuo Zhao||Chaoli Wang||Danny Z. Chen (2019). A New Ensemble Learning Framework for 3D Biomedical Image Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 5909-5916.

Hao Zheng||Yizhe Zhang||Lin Yang||Peixian Liang||Zhuo Zhao||Chaoli Wang||Danny Z. Chen. A New Ensemble Learning Framework for 3D Biomedical Image Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.5909-5916.

Hao Zheng||Yizhe Zhang||Lin Yang||Peixian Liang||Zhuo Zhao||Chaoli Wang||Danny Z. Chen. 2019. A New Ensemble Learning Framework for 3D Biomedical Image Segmentation. "Proceedings of the AAAI Conference on Artificial Intelligence". 5909-5916.

Hao Zheng||Yizhe Zhang||Lin Yang||Peixian Liang||Zhuo Zhao||Chaoli Wang||Danny Z. Chen. (2019) "A New Ensemble Learning Framework for 3D Biomedical Image Segmentation", Proceedings of the AAAI Conference on Artificial Intelligence, p.5909-5916

Hao Zheng||Yizhe Zhang||Lin Yang||Peixian Liang||Zhuo Zhao||Chaoli Wang||Danny Z. Chen, "A New Ensemble Learning Framework for 3D Biomedical Image Segmentation", AAAI, p.5909-5916, 2019.

Hao Zheng||Yizhe Zhang||Lin Yang||Peixian Liang||Zhuo Zhao||Chaoli Wang||Danny Z. Chen. "A New Ensemble Learning Framework for 3D Biomedical Image Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.5909-5916.

Hao Zheng||Yizhe Zhang||Lin Yang||Peixian Liang||Zhuo Zhao||Chaoli Wang||Danny Z. Chen. "A New Ensemble Learning Framework for 3D Biomedical Image Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 5909-5916.

Hao Zheng||Yizhe Zhang||Lin Yang||Peixian Liang||Zhuo Zhao||Chaoli Wang||Danny Z. Chen. A New Ensemble Learning Framework for 3D Biomedical Image Segmentation. AAAI[Internet]. 2019[cited 2023]; 5909-5916.


ISSN: 2374-3468


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