Improvement of TransUNet Using Word Patches Created from Different Dataset

Ayato Takama, Satoshi Kamiya, Kazuhiro Hotta

2024

Abstract

UNet is widely used in medical image segmentation, but it cannot extract global information sufficiently. On the other hand, TransUNet achieves better accuracy than conventional UNet by combining a CNN, which is good at local features, and a Transformer, which is good at global features. In general, TransUNet requires a large amount of training data, but there are constraints on training images in the medical area. In addition, the encoder of TransUNet uses a pre-trained model on ImageNet consisted of natural images, but the difference between medical images and natural images is a problem. In this paper, we propose a method to learn Word Patches from other medical datasets and effectively utilize them for training TransUNet. Experiments on the ACDC dataset containing 4 classes of 3D MRI images and the Synapse multi-organ segmentation dataset containing 9 classes of CT images show that the proposed method improved the accuracy even with small training data, and we showed that the performance of TransUNet is greatly improved by using Word Patches created from different medical datasets.

Paper Citation

in Harvard Style

Takama A., Kamiya S. and Hotta K. (2024). Improvement of TransUNet Using Word Patches Created from Different Dataset. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 378-385. DOI: 10.5220/0012419900003654

in Bibtex Style

@conference{icpram24,
author={Ayato Takama and Satoshi Kamiya and Kazuhiro Hotta},
title={Improvement of TransUNet Using Word Patches Created from Different Dataset},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={378-385},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012419900003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Improvement of TransUNet Using Word Patches Created from Different Dataset
SN - 978-989-758-684-2
AU - Takama A.
AU - Kamiya S.
AU - Hotta K.
PY - 2024
SP - 378
EP - 385
DO - 10.5220/0012419900003654
PB - SciTePress