On the effectiveness of Rotation-Equivariance in U-Net: A Benchmark for Image Segmentation

TMLR Paper3978 Authors

Abstract:

Numerous studies have recently focused on incorporating different variations of equivariance in Convolutional Neural Networks (CNNs). In particular, rotation-equivariance has gathered significant attention due to its relevance in many applications related to medical imaging, microscopic imaging, satellite imaging, industrial tasks, etc. While prior research has primarily focused on enhancing classification tasks with rotation equivariant CNNs, their impact on more complex architectures, such as U-Net for image segmentation, remains scarcely explored. Indeed, previous work interested in integrating rotation-equivariance into U-Net architecture have focused on solving specific applications with a limited scope. In contrast, this paper aims to provide a more exhaustive evaluation of rotation equivariant U-Net for image segmentation across a broader range of tasks. We benchmark their effectiveness against standard U-Net architectures, assessing improvements in terms of performance and sustainability (i.e., computational cost). Our evaluation focuses on datasets whose orientation
of objects of interest is arbitrary in the image (e.g., Kvasir-SEG), but also on more standard segmentation datasets (such as COCO-Stuff) as to explore the wider applicability of rotation equivariance beyond tasks undoubtedly concerned by rotation equivariance. The
main contribution of this work is to provide insights into the trade-offs and advantages of integrating rotation equivariance for segmentation tasks.

Submission Length: Long submission (more than 12 pages of main content)

Previous TMLR Submission Url: https://openreview.net/forum?id=qcBd6zMP2M&noteId=qcBd6zMP2M

Changes Since Last Submission:

We have removed the link to our github project that was initially present. One additional github link is still present (on page 5) and relates to a project that implements one of the technique used in the study. This is not our project. Therefore, we believe this does not break anonymity anymore.

We have adapted our manuscript according to the comments and requests provided by the reviewers. It mainly consists in the addition of 2 subsections in the background section of the paper, and in the addition of many references in the related works section.

Assigned Action Editor: ~Nicolas_THOME2

Submission Number: 3978