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Gastrointestinal stromal tumors diagnosis on multi-center endoscopic ultrasound images using multi-scale image normalization and transfer learning

BACKGROUND: Automated diagnosis of gastrointestinal stromal tumors’ (GISTs) cancerization is an effective way to improve the clinical diagnostic accuracy and reduce possible risks of biopsy. Although deep convolutional neural networks (DCNNs) have proven to be very effective in many image classifica...

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Autores principales: Liu, Chengcheng, Guo, Yi, Jiang, Fei, Xu, Leiming, Shen, Feng, Jin, Zhendong, Wang, Yuanyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOS Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028612/
https://www.ncbi.nlm.nih.gov/pubmed/35124583
http://dx.doi.org/10.3233/THC-228005
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author Liu, Chengcheng
Guo, Yi
Jiang, Fei
Xu, Leiming
Shen, Feng
Jin, Zhendong
Wang, Yuanyuan
author_facet Liu, Chengcheng
Guo, Yi
Jiang, Fei
Xu, Leiming
Shen, Feng
Jin, Zhendong
Wang, Yuanyuan
author_sort Liu, Chengcheng
collection PubMed
description BACKGROUND: Automated diagnosis of gastrointestinal stromal tumors’ (GISTs) cancerization is an effective way to improve the clinical diagnostic accuracy and reduce possible risks of biopsy. Although deep convolutional neural networks (DCNNs) have proven to be very effective in many image classification problems, there is still a lack of studies on endoscopic ultrasound (EUS) images of GISTs. It remains a substantial challenge mainly due to the data distribution bias of multi-center images, the significant inter-class similarity and intra-class variation, and the insufficiency of training data. OBJECTIVE: The study aims to classify GISTs into higher-risk and lower-risk categories. METHODS: Firstly, a novel multi-scale image normalization block is designed to perform same-size and same-resolution resizing on the input data in a parallel manner. A dilated mask is used to obtain a more accurate interested region. Then, we construct a multi-way feature extraction and fusion block to extract distinguishable features. A ResNet-50 model built based on transfer learning is utilized as a powerful feature extractor for tumors’ textural features. The tumor size features and the patient demographic features are also extracted respectively. Finally, a robust XGBoost classifier is trained on all features. RESULTS: Experimental results show that our proposed method achieves the AUC score of 0.844, which is superior to the clinical diagnosis performance. CONCLUSIONS: Therefore, the results have provided a solid baseline to encourage further researches in this field.
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spelling pubmed-90286122022-05-06 Gastrointestinal stromal tumors diagnosis on multi-center endoscopic ultrasound images using multi-scale image normalization and transfer learning Liu, Chengcheng Guo, Yi Jiang, Fei Xu, Leiming Shen, Feng Jin, Zhendong Wang, Yuanyuan Technol Health Care Research Article BACKGROUND: Automated diagnosis of gastrointestinal stromal tumors’ (GISTs) cancerization is an effective way to improve the clinical diagnostic accuracy and reduce possible risks of biopsy. Although deep convolutional neural networks (DCNNs) have proven to be very effective in many image classification problems, there is still a lack of studies on endoscopic ultrasound (EUS) images of GISTs. It remains a substantial challenge mainly due to the data distribution bias of multi-center images, the significant inter-class similarity and intra-class variation, and the insufficiency of training data. OBJECTIVE: The study aims to classify GISTs into higher-risk and lower-risk categories. METHODS: Firstly, a novel multi-scale image normalization block is designed to perform same-size and same-resolution resizing on the input data in a parallel manner. A dilated mask is used to obtain a more accurate interested region. Then, we construct a multi-way feature extraction and fusion block to extract distinguishable features. A ResNet-50 model built based on transfer learning is utilized as a powerful feature extractor for tumors’ textural features. The tumor size features and the patient demographic features are also extracted respectively. Finally, a robust XGBoost classifier is trained on all features. RESULTS: Experimental results show that our proposed method achieves the AUC score of 0.844, which is superior to the clinical diagnosis performance. CONCLUSIONS: Therefore, the results have provided a solid baseline to encourage further researches in this field. IOS Press 2022-02-25 /pmc/articles/PMC9028612/ /pubmed/35124583 http://dx.doi.org/10.3233/THC-228005 Text en © 2022 – The authors. Published by IOS Press. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License (https://creativecommons.org/licenses/by-nc/4.0/) , which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liu, Chengcheng
Guo, Yi
Jiang, Fei
Xu, Leiming
Shen, Feng
Jin, Zhendong
Wang, Yuanyuan
Gastrointestinal stromal tumors diagnosis on multi-center endoscopic ultrasound images using multi-scale image normalization and transfer learning
title Gastrointestinal stromal tumors diagnosis on multi-center endoscopic ultrasound images using multi-scale image normalization and transfer learning
title_full Gastrointestinal stromal tumors diagnosis on multi-center endoscopic ultrasound images using multi-scale image normalization and transfer learning
title_fullStr Gastrointestinal stromal tumors diagnosis on multi-center endoscopic ultrasound images using multi-scale image normalization and transfer learning
title_full_unstemmed Gastrointestinal stromal tumors diagnosis on multi-center endoscopic ultrasound images using multi-scale image normalization and transfer learning
title_short Gastrointestinal stromal tumors diagnosis on multi-center endoscopic ultrasound images using multi-scale image normalization and transfer learning
title_sort gastrointestinal stromal tumors diagnosis on multi-center endoscopic ultrasound images using multi-scale image normalization and transfer learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028612/
https://www.ncbi.nlm.nih.gov/pubmed/35124583
http://dx.doi.org/10.3233/THC-228005
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