Transfer learning for brain lesion segmentation via data transfer

Weiyan Guo, Xinru Zhang, Haowen Pang, Chuyang Ye*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Automated brain lesion segmentation using deep learning (DL) is crucial for understanding neurological diseases and aiding in diagnosis. Efficiently training a DL segmentation model often involves transfer learning (TL), where knowledge from a source task helps in the target task. Traditionally, this involves model weight transfer, initializing the target model with weights from a pretrained source model and then fine-tuning it with target data. However, this approach limits direct interaction between source data and the target task. This work introduces a new TL paradigm for brain lesion segmentation called Brain Lesion Transfer (BLeT). Instead of transferring model weights, BLeT directly utilizes source training data by transferring useful information to the target task. For a given target brain lesion segmentation task, it is assumed that annotated data for a similar source task is available. BLeT transfers lesions from the source training data to the target annotated data, creating additional, diverse training images, thereby enhancing the training of the target segmentation model. To address the challenge of different appearances in images from different tasks, BLeT includes a lesion appearance transformation method. This method adjusts the lesions from the source task to be compatible with the target images. Experiments on public datasets demonstrate that BLeT outperforms conventional TL methods based on model weights for brain lesion segmentation.

Original languageEnglish
Title of host publicationInternational Conference on Future of Medicine and Biological Information Engineering, MBIE 2024
EditorsYudong Yao, Xiaoou Li, Xia Yu
PublisherSPIE
ISBN (Electronic)9781510682825
DOIs
Publication statusPublished - 2024
Event2024 International Conference on Future of Medicine and Biological Information Engineering, MBIE 2024 - Shenyang, China
Duration: 10 Aug 202411 Aug 2024

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume13270
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2024 International Conference on Future of Medicine and Biological Information Engineering, MBIE 2024
Country/TerritoryChina
CityShenyang
Period10/08/2411/08/24

Keywords

  • Brain Lesion Segmentation
  • Deep Learning
  • Transfer Learning

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