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Yi Guo
Researcher at Fudan University
Publications - 67
Citations - 1530
Yi Guo is an academic researcher from Fudan University. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 15, co-authored 67 publications receiving 854 citations.
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Journal ArticleDOI
Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma.
TL;DR: The performance of DLR for predicting the mutation status of isocitrate dehydrogenase 1 (IDH1) was validated in a dataset of 151 patients with low-grade glioma and the AUC of IDH1 estimation was improved to 95% using DLR based on multiple-modality MR images, suggesting DLR could be a powerful way to extract deep information from medical images.
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Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model
TL;DR: The proposed automatic tumor segmentation method may be sufficiently accurate, robust, and efficient for medical ultrasound applications.
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Radiomics Analysis on Ultrasound for Prediction of Biologic Behavior in Breast Invasive Ductal Carcinoma
TL;DR: An automatic radiomics approach was proposed to assess the associations between quantitative ultrasound features and biologic characteristics and demonstrated a strong correlation between receptor status and subtypes.
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Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics
Jinhua Yu,Yinhui Deng,Tongtong Liu,Jin Zhou,Xiaohong Jia,Tianlei Xiao,Shichong Zhou,Jiawei Li,Yi Guo,Yuanyuan Wang,Jianqiao Zhou,Cai Chang +11 more
TL;DR: A transfer learning radiomics (TLR) model for preoperative prediction of LNM in PTC patients in a multicenter, cross-machine, multi-operator scenario produces a stable LNM prediction.
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Sonoelastomics for Breast Tumor Classification: A Radiomics Approach with Clustering-Based Feature Selection on Sonoelastography
TL;DR: For a data set containing 42 malignant and 75 benign tumors from 117 patients, seven selected sonoelastomic features achieved an area under the receiver operating characteristic curve of 0.917, revealing superiority over the principal component analysis, deep polynomial networks and manually selected features.