KEYWORDS: Transformers, Image segmentation, Data modeling, Education and training, Visual process modeling, Performance modeling, Image processing, Medical imaging
Transformer models have recently started gaining popularity in Computer Vision related tasks. Within Medical Image Segmentation, segmentation models such as TransUNet have incorporated transformer blocks alongside convolutional blocks while remaining faithful to the popular U-Net architecture. The present work utilizes attention maps to examines information flow within transformer blocks of three such segmentation models: (i) TransUNet, (ii) 2D CATS, and (iii) 2D UNETR. Based on the attention maps, compressed versions of these models are proposed which retain only as many transformer layers as are necessary for the model to achieve a global receptive field. The parameter saving is more than 60% whereas the dice metric does not drop by more than 5% compared to the original (uncompressed) model.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.