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Learning Bronchiole-Sensitive Airway Segmentation CNNs by Feature Recalibration and Attention Distillation

Yulei Qin Hao Zheng Yun Gu Xiaolin Huang Jie Yang Lihui Wang Yue-Min Zhu 1
1 MYRIAD - Modeling & analysis for medical imaging and Diagnosis
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
Abstract : Training deep convolutional neural networks (CNNs) for airway segmentation is challenging due to the sparse supervisory signals caused by severe class imbalance between long, thin airways and background. In view of the intricate pattern of tree-like airways, the segmentation model should pay extra attention to the morphology and distribution characteristics of airways. We propose a CNNs-based airway segmentation method that enjoys superior sensitivity to tenuous peripheral bronchioles. We first present a feature recalibration module to make the best use of learned features. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce the airway-specific representation learning. High-resolution attention maps with fine airway details are passing down from late layers to previous layers iteratively to enrich context knowledge. Extensive experiments demonstrate considerable performance gain brought by the two proposed modules. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance.
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https://hal.archives-ouvertes.fr/hal-03435078
Contributor : Yuemin Zhu Connect in order to contact the contributor
Submitted on : Thursday, November 18, 2021 - 3:24:00 PM
Last modification on : Wednesday, November 24, 2021 - 3:48:35 AM

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Yulei Qin, Hao Zheng, Yun Gu, Xiaolin Huang, Jie Yang, et al.. Learning Bronchiole-Sensitive Airway Segmentation CNNs by Feature Recalibration and Attention Distillation. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Oct 2020, Lima, Peru. pp.221-231, ⟨10.1007/978-3-030-59710-8_22⟩. ⟨hal-03435078⟩

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