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Junming Jian

Showing results (11-20 of 29) with videos related to

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The Journal of Dermatological Treatment|February 3, 2022
Deep learning-based fully automated diagnosis of melanocytic lesions by using whole slide imagesYongyang Bao, Jiayi Zhang, Xingyu Zhao, et al.
Australasian Physical & Engineering Sciences in Medicine|April 15, 2018
Fully convolutional networks (FCNs)-based segmentation method for colorectal tumors on T2-weighted magnetic resonance imagesJunming Jian, Fei Xiong, Wei Xia, et al.
Medical Physics|February 28, 2025
Boundary information-guided adversarial diffusion model for efficient unsupervised synthetic CT generationChangfei Gong, Junming Jian, Yuling Huang, et al.
Artificial Intelligence in Medicine|November 12, 2021
Multiple instance convolutional neural network with modality-based attention and contextual multi-instance learning pooling layer for effective differentiation between borderline and malignant epithelial ovarian tumorsJunming Jian, Wei Xia, Rui Zhang, et al.
Physical and Engineering Sciences in Medicine|June 8, 2026
Complementary roles of GPU-accelerated Monte Carlo and ArcCHECK in TomoTherapy quality assurancePanxia Wu, Wenheng Zheng, Longfei Xu, et al.
European Radiology|September 26, 2021
CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapyFengchang Yang, Jiayi Zhang, Liu Zhou, et al.
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|October 5, 2021
3D multi-scale, multi-task, and multi-label deep learning for prediction of lymph node metastasis in T1 lung adenocarcinoma patients' CT imagesXingyu Zhao, Xiang Wang, Wei Xia, et al.
Medical Physics|April 12, 2019
Full convolutional network based multiple side-output fusion architecture for the segmentation of rectal tumors in magnetic resonance images: A multi-vendor studyMengmeng Wang, Peiyi Xie, Zhao Ran, et al.
European Radiology|March 20, 2019
Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT?Xiang Wang, Xingyu Zhao, Qiong Li, et al.
Radiology. Artificial Intelligence|February 14, 2024
Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRIWei Xia, Dandan Li, Wenguang He, et al.
Pageof 3

Showing results (11-20 of 29) with videos related to

Sort By:
Pageof 3
The Journal of Dermatological Treatment|February 3, 2022
Deep learning-based fully automated diagnosis of melanocytic lesions by using whole slide imagesYongyang Bao, Jiayi Zhang, Xingyu Zhao, et al.
Australasian Physical & Engineering Sciences in Medicine|April 15, 2018
Fully convolutional networks (FCNs)-based segmentation method for colorectal tumors on T2-weighted magnetic resonance imagesJunming Jian, Fei Xiong, Wei Xia, et al.
Medical Physics|February 28, 2025
Boundary information-guided adversarial diffusion model for efficient unsupervised synthetic CT generationChangfei Gong, Junming Jian, Yuling Huang, et al.
Artificial Intelligence in Medicine|November 12, 2021
Multiple instance convolutional neural network with modality-based attention and contextual multi-instance learning pooling layer for effective differentiation between borderline and malignant epithelial ovarian tumorsJunming Jian, Wei Xia, Rui Zhang, et al.
Physical and Engineering Sciences in Medicine|June 8, 2026
Complementary roles of GPU-accelerated Monte Carlo and ArcCHECK in TomoTherapy quality assurancePanxia Wu, Wenheng Zheng, Longfei Xu, et al.
European Radiology|September 26, 2021
CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapyFengchang Yang, Jiayi Zhang, Liu Zhou, et al.
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|October 5, 2021
3D multi-scale, multi-task, and multi-label deep learning for prediction of lymph node metastasis in T1 lung adenocarcinoma patients' CT imagesXingyu Zhao, Xiang Wang, Wei Xia, et al.
Medical Physics|April 12, 2019
Full convolutional network based multiple side-output fusion architecture for the segmentation of rectal tumors in magnetic resonance images: A multi-vendor studyMengmeng Wang, Peiyi Xie, Zhao Ran, et al.
European Radiology|March 20, 2019
Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT?Xiang Wang, Xingyu Zhao, Qiong Li, et al.
Radiology. Artificial Intelligence|February 14, 2024
Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRIWei Xia, Dandan Li, Wenguang He, et al.
Pageof 3