Next Article in Journal
A Multi-Task Deep Learning Framework for Characterizing Beating Behavior and Synchrony in Cardiomyocyte Clusters
Previous Article in Journal
Mapping the Global Trajectory and Key Trends of Temporal Interference Stimulation
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Bioengineering
Volume 13
Issue 7
10.3390/bioengineering13070740
bioengineering-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Deep Learning Framework for Gastric Cancer Cell Segmentation with Multi-Scale Attention Mechanisms

by
Xinyu Zhao
1,
Jin Liu
1,*,
Jingru Zhang
2,*,
Damin Ding
1,
Haima Yang
3 and
Bo Huang
1
1
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2
Centre for Instructional Technology and Multimedia, Universiti Sains Malaysia (USM), Gelugor 11700, Pulau Pinang, Malaysia
3
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
*
Authors to whom correspondence should be addressed.
Bioengineering 2026, 13(7), 740; https://doi.org/10.3390/bioengineering13070740 (registering DOI)
Submission received: 19 May 2026 / Revised: 17 June 2026 / Accepted: 24 June 2026 / Published: 25 June 2026
(This article belongs to the Section Biomedical Engineering and Biomaterials)
Versions Notes

Abstract

The accurate segmentation of gastric cancer cells is important in pathology for diagnosing and detecting diseases early. However, current approaches still suffer from limitations such as expensive annotation, fuzzy lesion boundaries, and weak feature expression. In order to solve these problems, we present MSAF-Net, a novel U-Net framework optimized both architecturally and in terms of the loss function. In particular, we incorporate a Multi-scale Dilated Pooling Fusion Block into the encoder stage to achieve enhanced interaction of multi-paths and thus improve features’ diversity and boundary sensitivity. We also introduce a Dual-Channel Attention Block in place of traditional convolution block in the decoder stage to restore better details and reconstruct the fuzzy boundaries. Meanwhile, a Diagonal Mahalanobis Consistency Loss is incorporated into our framework to facilitate class compactness. Experiments performed on the SEED-Gastric Carcinoma Stage 1 dataset show that the designed algorithm can reach 0.776 in Dice score and 0.821 in Accuracy, which outperforms the baseline method U-Net. It is clear that these results have shown the effectiveness and robustness of our proposed approach. The introduced algorithm allows for more precise quantification of gastric cancer cell morphology.
Keywords: channel attention; deep learning; feature fusion; gastrointestinal channel attention; deep learning; feature fusion; gastrointestinal

Share and Cite

MDPI and ACS Style

Zhao, X.; Liu, J.; Zhang, J.; Ding, D.; Yang, H.; Huang, B. A Deep Learning Framework for Gastric Cancer Cell Segmentation with Multi-Scale Attention Mechanisms. Bioengineering 2026, 13, 740. https://doi.org/10.3390/bioengineering13070740

AMA Style

Zhao X, Liu J, Zhang J, Ding D, Yang H, Huang B. A Deep Learning Framework for Gastric Cancer Cell Segmentation with Multi-Scale Attention Mechanisms. Bioengineering. 2026; 13(7):740. https://doi.org/10.3390/bioengineering13070740

Chicago/Turabian Style

Zhao, Xinyu, Jin Liu, Jingru Zhang, Damin Ding, Haima Yang, and Bo Huang. 2026. "A Deep Learning Framework for Gastric Cancer Cell Segmentation with Multi-Scale Attention Mechanisms" Bioengineering 13, no. 7: 740. https://doi.org/10.3390/bioengineering13070740

APA Style

Zhao, X., Liu, J., Zhang, J., Ding, D., Yang, H., & Huang, B. (2026). A Deep Learning Framework for Gastric Cancer Cell Segmentation with Multi-Scale Attention Mechanisms. Bioengineering, 13(7), 740. https://doi.org/10.3390/bioengineering13070740

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop