Adaptive Graph Convolutional Networks for Medical Image Segmentation

Shurong Chai, Rahul Kumar Jain, Yinhao Li, Jiaqing Liu, Tomoko Tateyama, Yen Wei Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Medical image segmentation is very essential for computer-aided diagnosis in the field of medical imaging. In the last decade, Deep Learning-based frameworks (e.g., UNet) have been widely used in medical applications such as image segmentation tasks. Recently, numerous Transformer-based frameworks are presented for the image segmentation tasks as their design can utilize long-range dependencies. Transformer's design has a weak inductive bias since it does not take advantage of local relationships between pixels and lacks scale invariance. Consequently, Transformers require large datasets for convergence whereas the availability of massive medical datasets is challenging. In this paper, we present a graph-based approach replacing Transformer design to capture long-range dependencies and reduce computational cost. Our proposed framework achieves competitive performance using publicly available dataset Synapse.

Original languageEnglish
Title of host publication2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350324471
DOIs
Publication statusPublished - 2023
Event45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Sydney, Australia
Duration: 24-07-202327-07-2023

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023
Country/TerritoryAustralia
CitySydney
Period24-07-2327-07-23

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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