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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation

February 1, 2023

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Authors

Kang Li

CUHK


Lequan Yu

CUHK


Shujun Wang

CUHK


Pheng-Ann Heng

CUHK, SIAT-CAS


DOI:

10.1609/aaai.v34i01.5421


Abstract:

The success of deep convolutional neural networks is partially attributed to the massive amount of annotated training data. However, in practice, medical data annotations are usually expensive and time-consuming to be obtained. Considering multi-modality data with the same anatomic structures are widely available in clinic routine, in this paper, we aim to exploit the prior knowledge (e.g., shape priors) learned from one modality (aka., assistant modality) to improve the segmentation performance on another modality (aka., target modality) to make up annotation scarcity. To alleviate the learning difficulties caused by modality-specific appearance discrepancy, we first present an Image Alignment Module (IAM) to narrow the appearance gap between assistant and target modality data. We then propose a novel Mutual Knowledge Distillation (MKD) scheme to thoroughly exploit the modality-shared knowledge to facilitate the target-modality segmentation. To be specific, we formulate our framework as an integration of two individual segmentors. Each segmentor not only explicitly extracts one modality knowledge from corresponding annotations, but also implicitly explores another modality knowledge from its counterpart in mutual-guided manner. The ensemble of two segmentors would further integrate the knowledge from both modalities and generate reliable segmentation results on target modality. Experimental results on the public multi-class cardiac segmentation data, i.e., MM-WHS 2017, show that our method achieves large improvements on CT segmentation by utilizing additional MRI data and outperforms other state-of-the-art multi-modality learning methods.

Topics: AAAI

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HOW TO CITE:

Kang Li||Lequan Yu||Shujun Wang||Pheng-Ann Heng Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation Proceedings of the AAAI Conference on Artificial Intelligence (2020) 775-783.

Kang Li||Lequan Yu||Shujun Wang||Pheng-Ann Heng Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation AAAI 2020, 775-783.

Kang Li||Lequan Yu||Shujun Wang||Pheng-Ann Heng (2020). Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation. Proceedings of the AAAI Conference on Artificial Intelligence, 775-783.

Kang Li||Lequan Yu||Shujun Wang||Pheng-Ann Heng. Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.775-783.

Kang Li||Lequan Yu||Shujun Wang||Pheng-Ann Heng. 2020. Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation. "Proceedings of the AAAI Conference on Artificial Intelligence". 775-783.

Kang Li||Lequan Yu||Shujun Wang||Pheng-Ann Heng. (2020) "Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation", Proceedings of the AAAI Conference on Artificial Intelligence, p.775-783

Kang Li||Lequan Yu||Shujun Wang||Pheng-Ann Heng, "Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation", AAAI, p.775-783, 2020.

Kang Li||Lequan Yu||Shujun Wang||Pheng-Ann Heng. "Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.775-783.

Kang Li||Lequan Yu||Shujun Wang||Pheng-Ann Heng. "Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 775-783.

Kang Li||Lequan Yu||Shujun Wang||Pheng-Ann Heng. Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation. AAAI[Internet]. 2020[cited 2023]; 775-783.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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