Lightseg: Efficient Yet Effective Medical Image Segmentation

Most Husne Jahan, Abdullah Al Zubaer Imran

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

2 Scopus citations

Abstract

While recent development in deep learning-based medical image segmentation has been fascinating, effectiveness mostly comes with the expense of expensive computing resources. In search of more affordable and convenient solutions, we propose a lightweight and faster yet effective medical image segmentation approach namely LightSeg. LightSeg leverages separable convolutional layers to decrease the model parameters and an attention mechanism to maintain segmentation quality. Our experimental evaluations on two different backbone networks (U-Net and ResU-Net) in segmenting the lungs from two publicly available chest X-ray datasets demonstrate the robustness of LightSeg while substantially reducing the network parameters.

Original languageEnglish
Title of host publicationISBI 2022 - Proceedings
Subtitle of host publication2022 IEEE International Symposium on Biomedical Imaging
ISBN (Electronic)9781665429238
DOIs
StatePublished - 2022
Event19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 - Kolkata, India
Duration: Mar 28 2022Mar 31 2022

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2022-March
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference19th IEEE International Symposium on Biomedical Imaging, ISBI 2022
Country/TerritoryIndia
CityKolkata
Period3/28/223/31/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Channel attention
  • Lungs
  • Segmentation
  • U-Net
  • chest X-ray

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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