research-article

TransUNet-Lite: A Robust Approach to Cell Nuclei Segmentation

Authors:

Muhammad Salman Khan,

Soo Young Park,

Soon Ki JungAuthors Info & Claims

ICMHI '23: Proceedings of the 2023 7th International Conference on Medical and Health Informatics

Pages 251 - 258

https://doi.org/10.1145/3608298.3608344

Published: 18 October 2023 Publication History

Abstract

Deep convolutional neural networks have demonstrated superior performance in a variety of vision tasks. For biomedical applications, these methods suffer from problems such as predicting reliable segmentation masks for variable size input images, insufficient data and imbalanced datasets. This paper introduces an efficient and lightweight TransUNet, termed as TransUNet-Lite, that exploits rich feature representations produced by the convolution-based feature extractor, an external attention module instead of conventional self-attention, a fast token selector module, and skip connections from the feature extractor to the decoder to provide lost rich contextual information. The proposed network takes patches as input rather than resized images that fail to care for the original aspect ratio. For the nuclei segmentation task on the 2018 Science Bowl dataset, our TransUNet-Lite outperformed other SOTA networks, with the highest DSC of 93.08% and IoU of 87.95%. The results of our experiments provide insight into the impact of certain network design decisions. By configuring a transformer in a simplistic and efficient manner, it is possible to achieve segmentation quality that is at least equal to SOTA network architectures.

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Cited By

Ali SLee YPark STak WJung S(2025)Edge-Preserving Probabilistic Downsampling for Reliable Medical Segmentation in Resource-Constrained EnvironmentsIEEE Access10.1109/ACCESS.2025.353628613(21620-21634)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3536286
Kim GBlekanov IEzhov FKovalenko LLarin ERazumilov EPugin KDadashov MPyagay VShmatov D(2024)Artificial intelligence methods in cardiovascular surgery and diagnosis of pathology of the aorta and aortic valve (literature review)Siberian Journal of Clinical and Experimental Medicine10.29001/2073-8552-2024-39-2-36-4539:2(36-45)Online publication date: 11-Jul-2024
https://doi.org/10.29001/2073-8552-2024-39-2-36-45

Index Terms

TransUNet-Lite: A Robust Approach to Cell Nuclei Segmentation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation

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ICMHI '23: Proceedings of the 2023 7th International Conference on Medical and Health Informatics

May 2023

386 pages

ISBN:9798400700712

DOI:10.1145/3608298

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 18 October 2023

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ICMHI 2023

ICMHI 2023: 2023 the 7th International Conference on Medical and Health Informatics

May 12 - 14, 2023

Kyoto, Japan

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Cited By

Ali SLee YPark STak WJung S(2025)Edge-Preserving Probabilistic Downsampling for Reliable Medical Segmentation in Resource-Constrained EnvironmentsIEEE Access10.1109/ACCESS.2025.353628613(21620-21634)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3536286
Kim GBlekanov IEzhov FKovalenko LLarin ERazumilov EPugin KDadashov MPyagay VShmatov D(2024)Artificial intelligence methods in cardiovascular surgery and diagnosis of pathology of the aorta and aortic valve (literature review)Siberian Journal of Clinical and Experimental Medicine10.29001/2073-8552-2024-39-2-36-4539:2(36-45)Online publication date: 11-Jul-2024
https://doi.org/10.29001/2073-8552-2024-39-2-36-45

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