Gated axial transformer network for mass segmentation in mammographic

Yutong Zhong; Yan Piao

doi:10.1117/12.3009417

11 October 2023 Gated axial transformer network for mass segmentation in mammographic

Yutong Zhong, Yan Piao

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 129180L (2023) https://doi.org/10.1117/12.3009417
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

Mammography is main tests used for breast cancer risk assessment. However, due to the high similarity of the mass to breast tissue, blurring of the mass edges causes the task of segmenting the breast mass on mammograms to be challenging. To reduce computational costs and the workloads of radiologists, deep learning techniques based on computer vision have become a common implementation in the field of medical image segmentation. However, owing to the locality of the convolutional operation, the neural networks cannot effectively learn global and remote semantic information. We propose a novel gated axial transformer network (GATNet) framework for the mass segmentation of mammograms. GATNet uses an encoder-decoder structure. First, we use axial attention to decompose 2D self-attention into two one-dimensional self attentions. Second, we construct an efficient location-sensitive gated transformer module for image features to establish remote contextual dependencies. We conducted evaluations the proposed GATNet on two publicly available breast mass segmentation datasets. The average Dice similarity coefficients between the GATNet results and INBreast and CBISDDSM data were 80.98% and 83.63%, respectively. The experimental results indicate that GATNet effectively reduces both the computational and spatial complexity of the medical image segmentation network, demonstrating its remarkable performance.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yutong Zhong and Yan Piao "Gated axial transformer network for mass segmentation in mammographic", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 129180L (11 October 2023); https://doi.org/10.1117/12.3009417

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Transformers

Mammography

Data modeling

Medical imaging

Visual process modeling

Education and training

Show All Keywords

Keywords/Phrases

Search In:

Publication Years