Jing Qin
Beihua University(CN)Ministry of Education of the People's Republic of China(CN)Central South University(CN)Hong Kong Polytechnic University(HK)University of Kentucky(US)University of Electronic Science and Technology of China(CN)Shandong University(CN)Tianjin University(CN)University of Southern Denmark(DK)Guizhou University(CN)Chinese Academy of Medical Sciences & Peking Union Medical College(CN)Harbin Institute of Technology(CN)Sichuan University(CN)Fudan University(CN)Chang'an University(CN)University of Würzburg(DE)Peking Union Medical College Hospital(CN)Odense Municipality(DK)China Aerospace Science and Technology Corporation(CN)UNSW Sydney(AU)China Electric Equipment Group (China)(CN)Tianjin Internal Combustion Engine Research Institute(CN)The First People's Hospital of Changde(CN)First Affiliated Hospital of Chongqing Medical University(CN)Second Affiliated Hospital of Nanjing Medical University(CN)China Power Engineering Consulting Group (China)(CN)Second Xiangya Hospital of Central South University(CN)Heilongjiang Institute of Technology(CN)Nanjing Medical University(CN)Chongqing Medical University(CN)Shaanxi Normal University(CN)
Publications by Year
Research Areas
Medical Image Segmentation Techniques, Advanced Neural Network Applications, AI in cancer detection, Radiomics and Machine Learning in Medical Imaging, Computer Graphics and Visualization Techniques
Most-Cited Works
- → Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks(2016)1,118 cited
- → VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images(2017)782 cited
- → Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans(2016)745 cited
- → Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks(2016)681 cited
- → 3D deeply supervised network for automated segmentation of volumetric medical images(2017)628 cited
- → Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection(2016)587 cited