Articles with public access mandates - Jianfei LiuLearn more
Not available anywhere: 29
A cascade 3D U‐Net for dose prediction in radiotherapy
S Liu, J Zhang, T Li, H Yan, J Liu
Medical physics 48 (9), 5574-5582, 2021
Mandates: National Natural Science Foundation of China
Predicting voxel-level dose distributions for esophageal radiotherapy using densely connected network with dilated convolutions
J Zhang, S Liu, H Yan, T Li, R Mao, J Liu
Physics in Medicine & Biology 65 (20), 205013, 2020
Mandates: National Natural Science Foundation of China
From active shape model to active optical flow model: a shape-based approach to predicting voxel-level dose distributions in spine SBRT
J Liu, QJ Wu, JP Kirkpatrick, FF Yin, L Yuan, Y Ge
Physics in Medicine & Biology 60 (5), N83, 2015
Mandates: US National Institutes of Health
Convolutional neural network‐based dosimetry evaluation of esophageal radiation treatment planning
D Jiang, H Yan, N Chang, T Li, R Mao, C Du, B Guo, J Liu
Medical Physics 47 (10), 4735-4742, 2020
Mandates: National Natural Science Foundation of China
Voxel-level radiotherapy dose prediction using densely connected network with dilated convolutions
J Zhang, S Liu, T Li, R Mao, C Du, J Liu
Artificial Intelligence in Radiation Therapy: First International Workshop …, 2019
Mandates: National Natural Science Foundation of China
Automatic cell segmentation using mini-u-net on fluorescence in situ hybridization images
J Shen, T Li, C Hu, H He, J Liu
Medical Imaging 2019: Computer-Aided Diagnosis 10950, 721-727, 2019
Mandates: National Natural Science Foundation of China
Automatic segmentation and 3D reconstruction of spine based on FCN and marching cubes in CT volumes
L Fang, J Liu, J Liu, R Mao
2018 10th International Conference on Modelling, Identification and Control …, 2018
Mandates: National Natural Science Foundation of China
A fully automatic framework for cell segmentation on non-confocal adaptive optics images
J Liu, A Dubra, J Tam
Medical Imaging 2016: Computer-Aided Diagnosis 9785, 654-660, 2016
Mandates: US National Institutes of Health
Automated classification of intravenous contrast enhancement phase of ct scans using residual networks
A Anand, J Liu, TC Shen, WM Linehan, PA Pinto, RM Summers
Medical Imaging 2023: Computer-Aided Diagnosis 12465, 129-134, 2023
Mandates: US National Institutes of Health
Body location embedded 3D U-Net (BLE-U-Net) for ovarian cancer ascites segmentation on CT scans
MK Nag, J Liu, L Liu, SY Shin, S Lee, JM Lee, RM Summers
18th International Symposium on Medical Information Processing and Analysis …, 2023
Mandates: US National Institutes of Health
Computer-aided detection of human cone photoreceptor inner segments using multi-scale circular voting
J Liu, A Dubra, J Tam
Medical Imaging 2016: Computer-Aided Diagnosis 9785, 323-329, 2016
Mandates: US National Institutes of Health
Tumor sensitive matching flow: an approach for ovarian cancer metastasis detection and segmentation
J Liu, S Wang, MG Linguraru, RM Summers
Abdominal Imaging. Computational and Clinical Applications: 4th …, 2012
Mandates: US National Institutes of Health
Improved multi-modal patch based lymphoma segmentation with negative sample augmentation and label guidance on PET/CT scans
L Liu, J Liu, MK Nag, N Hasani, SY Shin, SS Paravastu, B Saboury, J Xiao, ...
International Workshop on Multiscale Multimodal Medical Imaging, 121-129, 2022
Mandates: US National Institutes of Health
A dual-branch network with mixed and self-supervision for medical image segmentation: an application to segment edematous adipose tissue
J Liu, O Shafaat, RM Summers
Workshop on medical image learning with limited and noisy data, 158-167, 2023
Mandates: US National Institutes of Health
Development of multiscale 3D residual U-net to segment edematous adipose tissue by leveraging annotations from non-edematous adipose tissue
J Liu, O Shafaat, RM Summers
18th International Symposium on Medical Information Processing and Analysis …, 2023
Mandates: US National Institutes of Health
Spatially aware deep learning improves identification of retinal pigment epithelial cells with heterogeneous fluorescence levels visualized using adaptive optics
J Liu, YJ Han, T Liu, J Tam
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and …, 2020
Mandates: US National Institutes of Health
An augmented cell segmentation in fluorescent in situ hybridization images
J Shen, T Li, C Hu, H He, D Jiang, J Liu
2019 41st Annual International Conference of the IEEE Engineering in …, 2019
Mandates: National Natural Science Foundation of China
Deep learning based dosimetry evaluation at organs-at-risk in esophageal radiation treatment planning
D Jiang, T Li, R Mao, C Du, J Liu
2019 41st Annual International Conference of the IEEE Engineering in …, 2019
Mandates: National Natural Science Foundation of China
Automatic cell segmentation and signal detection in fluorescent in situ hybridization
J Wang, J Liu, J Liu, H Yan, R Mao
Proceedings of 2018 Chinese Intelligent Systems Conference: Volume II, 285-293, 2019
Mandates: National Natural Science Foundation of China
Noninvasive infrared autofluorescence imaging of intrinsic fluorophores in the human retina at cellular-level resolution using adaptive optics
J Tam, M Droettboom, J Liu, HW Jung
Bio-optics: design and application, JTu5A. 1, 2017
Mandates: US National Institutes of Health
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