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Knowledge transfer between brain lesion segmentation tasks with increased model capacity.

Authors
  • Liu, Yanlin1
  • Cui, Wenhui2
  • Ha, Qing3
  • Xiong, Xiaoliang3
  • Zeng, Xiangzhu4
  • Ye, Chuyang5
  • 1 School of Information and Electronics, Beijing Institute of Technology, Beijing, China. , (China)
  • 2 School of Computer Science and Technology, Xidian University, Xi'an, China. , (China)
  • 3 Deepwise AI Lab, Beijing, China. , (China)
  • 4 Department of Radiology, Peking University Third Hospital, Beijing, China. , (China)
  • 5 School of Information and Electronics, Beijing Institute of Technology, Beijing, China. Electronic address: [email protected] , (China)
Type
Published Article
Journal
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Publication Date
Dec 25, 2020
Volume
88
Pages
101842–101842
Identifiers
DOI: 10.1016/j.compmedimag.2020.101842
PMID: 33387812
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Convolutional neural networks (CNNs) have become an increasingly popular tool for brain lesion segmentation in recent years due to its accuracy and efficiency. However, CNN-based brain lesion segmentation generally requires a large amount of annotated training data, which can be costly for medical imaging. In many scenarios, only a few annotations of brain lesions are available. One common strategy to address the issue of limited annotated data is to transfer knowledge from a different yet relevant source task, where training data is abundant, to the target task of interest. Typically, a model can be pretrained for the source task, and then fine-tuned with the scarce training data associated with the target task. However, classic fine-tuning tends to make small modifications to the pretrained model, which could hinder its adaptation to the target task. Fine-tuning with increased model capacity has been shown to alleviate this negative impact in image classification problems. In this work, we extend the strategy of fine-tuning with increased model capacity to the problem of brain lesion segmentation, and then develop an advanced version that is better suitable for segmentation problems. First, we propose a vanilla strategy of increasing the capacity, where, like in the classification problem, the width of the network is augmented during fine-tuning. Second, because unlike image classification, in segmentation problems each voxel is associated with a labeling result, we further develop a spatially adaptive augmentation strategy during fine-tuning. Specifically, in addition to the vanilla width augmentation, we incorporate a module that computes a spatial map of the contribution of the information given by width augmentation in the final segmentation. For demonstration, the proposed method was applied to ischemic stroke lesion segmentation, where a model pretrained for brain tumor segmentation was fine-tuned, and the experimental results indicate the benefit of our method. Copyright © 2020 Elsevier Ltd. All rights reserved.

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