Medical Image Segmentation Using Transformer Networks

Author:

Karimi Davood1ORCID,Dou Haoran2,Gholipour Ali1ORCID

Affiliation:

1. Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

2. Centre for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, U.K.

Funder

National Institute of Biomedical Imaging and Bioengineering

National Institute of Neurological Disorders and Stroke of the National Institutes of Health

Office of the Director of the NIH

Technological Innovations in Neuroscience Award from the McKnight Foundation

EPSRC Doctoral Training Partnership (DTP) Studentship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Reference52 articles.

1. Recalibrating Fully Convolutional Networks With Spatial and Channel “Squeeze and Excitation” Blocks

2. Attention U-Net: Learning where to look for the pancreas;oktay;arXiv 1804 03999,2018

3. An image is worth 16×16 words: Transformers for image recognition at scale;dosovitskiy;arXiv 2010 11929,2020

4. Transformers in vision: A survey;khan;arXiv 2101 01169,2021

5. Attention is all you need;vaswani;arXiv 1706 03762,2017

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