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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
IOS Press
2022
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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. |
format | Online Article Text |
id | pubmed-9028612 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | IOS Press |
record_format | MEDLINE/PubMed |
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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