TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Multi-parametric MRIs based assessment of Hepatocellular Carcinoma Differentiation with Multi-scale ResNet |
| 영문제목(English Title) |
Multi-parametric MRIs based assessment of Hepatocellular Carcinoma Differentiation with Multi-scale ResNet |
| 저자(Author) |
Xibin Jia
Yujie Xiao
Dawei Yang
Zhenghan Yang
Chen Lu
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| 원문수록처(Citation) |
VOL 13 NO. 10 PP. 5179 ~ 5196 (2019. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
To explore an effective non-invasion medical imaging diagnostics approach for hepatocellular carcinoma (HCC), we propose a method based on adopting the multiple technologies with the multi-parametric data fusion, transfer learning, and multi-scale deep feature extraction. Firstly, to make full use of complementary and enhancing the contribution of different modalities viz. multi-parametric MRI images in the lesion diagnosis, we propose a data-level fusion strategy. Secondly, based on the fusion data as the input, the multi-scale residual neural network with SPP (Spatial Pyramid Pooling) is utilized for the discriminative feature representation learning. Thirdly, to mitigate the impact of the lack of training samples, we do the pre-training of the proposed multi-scale residual neural network model on the natural image dataset and the fine-tuning with the chosen multi-parametric MRI images as complementary data. The comparative experiment results on the dataset from the clinical cases show that our proposed approach by employing the multiple strategies achieves the highest accuracy of 0.847±0.023 in the classification problem on the HCC differentiation. In the problem of discriminating the HCC lesion from the non-tumor area, we achieve a good performance with accuracy, sensitivity, specificity and AUC (area under the ROC curve) being 0.981±0.002, 0.981±0.002, 0.991±0.007 and 0.999±0.0008, respectively.
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| 키워드(Keyword) |
Multi-parametric MRI
data fusion
transfer learning
deep learning
hepatocellular carcinoma differentiation
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| 파일첨부 |
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