News
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Feb 2023: DyLiN paper is accepted by CVPR2023, see you in Vancouver!
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Feb 2023: SubZero abstract is accepted by ISMRM2023 as a power pitch, see you in Toronto!
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Dec 2022: CoNFies paper has been nominated as a best paper candidate!
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Nov 2022: Non-pooling Network paper won Best Paper Award at MICAD 2022!
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Nov 2022: Won gold medal at the 8th China International College Students' 'Internet+' Innovation and Entrepreneurship Competition!
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Sep 2022: CoNFies paper is accepted by FG2023, see you in Hawaii!
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May 2022: I am joining Fujitsu this summer as a machine learning intern, working on controllable NeRF.
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Feb 2022: One paper is accepted by Magnetic Resonance in Medicine!
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Aug 2021: Start my graduate study at CMU RI!
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May 2021: I am joining Sangfor this summer as a machine learning engineer, working on evading web application firewalls with reinforcement learning.
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Mar 2021: One paper is accepted by Nature Communications!
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Feb 2021: eRAKI abstract is accepted by ISMRM2021 as an oral!
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Apr 2020: MixModule paper is accepted by ISBI2020!
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Nov 2019: One paper is accepted by Annals of Surgery!
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Research
I have rich experiences in computer vision, MRI reconstruction and medical image analysis.
I would like to explore the possibility of AI technology and its applications (e.g. 3D Scene Understanding , Healthcare, etc).
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Selected Publications
* refers to co-first author. Please refer to my google scholar for more details.
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DyLiN: Making Light Field Networks Dynamic
Heng Yu,
Joel Julin,
Zoltan Adam Milacski,
Koichiro Niinuma,
László A. Jeni,
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
paper
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project page
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code
We propose propose the Dynamic Light Field Network (DyLiN) method that can handle non-rigid deformations, including topological changes,
which outperformed state-of-the art methods in terms of visual fidelity and compute complexity.
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Unsupervised Style-based Explicit 3D Face Reconstruction from Single Image
Heng Yu,
Zoltan Adam Milacski,
László A. Jeni,
CVPR workshop, 2023
paper
We propose a general adversarial learning framework for solving Unsupervised 2D to Explicit 3D Style Transfer.
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CoNFies: Controllable Neural Face Avatars
Heng Yu,
Koichiro Niinuma
László A. Jeni,
International Conference on Automatic Face and Gesture Recognition (FG), 2023 - Best Paper Award Finalist
paper
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project page
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code
We propose a fully-automatic controllable neural representation for face self-portraits.
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Scan-specific artifact reduction in k-space (SPARK) neural networks synergize with physics-based reconstruction to accelerate MRI
Yamin Arefeen, Onur Beker,
Jaejin Cho, Heng Yu,
Elfar Adalsteinsson, Berkin Bilgic
Magnetic Resonance in Medicine (MRM), 2022
paper
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code
We develop a scan-specific model that estimates and corrects k-space errors made when reconstructing accelerated MRI data.
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eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction
Heng Yu, Zijing Dong,
Yamin Arefeen, Congyu Liao,
Kawin Setsompop, Berkin Bilgic
Proceedings of the 29th Annual Meeting of ISMRM, 2021 - Oral Presentation
paper
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code
We accelerate RAKI by more than 200 times by directly learning a coil-combined target.
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Predicting treatment response from longitudinal images using multi-task deep learning
Cheng Jin*, Heng Yu*, Jia Ke*, Peirong Ding*,
Yongju Yi, Xiaofeng Jiang, Xin Duan, Jinghua Tang, Daniel T. Chang,
Xiaojian Wu, Feng Gao, Ruijiang Li
Nature Communications, 2021
paper
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code
We present a multi-task deep learning approach that allows simultaneous tumor segmentation and response prediction
of pathologic complete response after neoadjuvant chemoradiotherapy.
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MixModule: Mixed CNN Kernel Module for Medical Image Segmentation
Heng Yu, Xue Feng, Ziwen Wang, Hao Sun
IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020
paper
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code
We use mixed kernels to improve the performance of existing medical image segmentation networks.
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Development and validation of a deep learning CT signature to predict survival and chemotherapy benefit in gastric cancer: a multicenter, retrospective study.
Yuming Jiang*,
Cheng Jin*, Heng Yu*,
Jia Wu*,
Chuanli Chen, Qingyu Yuan, Weicai Huang, Yanfeng Hu, Yikai Xu, Zhiwei Zhou,
George A. Fisher Jr.,
Guoxin Li,
Ruijiang Li
Annals of surgery, 2020
paper
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code
We propose a novel deep neural network (S-net) to construct a CT signature for predicting disease-free survival (DFS) and overall survival.
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Deep learning analysis of the primary tumour and the prediction of lymph node metastases in gastric cancer.
C Jin*,
Y Jiang*, H Yu*,
W Wang, B Li, C Chen, Q Yuan, Y Hu, Y Xu, Z Zhou,
G Li,
R Li
British Journal of Surgery, 2020
paper
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code
We develop a deep learning system for predicting lymph node metastasis in multiple nodal stations
based on preoperative CT images in patients with gastric cancer.
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SmartPartNet: Part-Informed Person Detection for Body-Worn Smartphones.
H Yu*,
Eshed Ohn-Bar,
Donghyun Yoo,
Kris Kitani
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2018
paper
We develop an image-based person detection algorithm for wearable computing using commodity smartphones.
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Left atrial appendage segmentation using fully convolutional neural networks and modified three-dimensional conditional random fields.
Cheng Jin, Jianjiang Feng , Lei Wang,
Heng Yu, Jiang Liu,
Jiwen Lu,
Jie Zhou
IEEE Journal of Biomedical and Health Informatics (JBHI), 2018
paper
We propose a robust method for automatic left atrial appendage segmentation on computed tomographic angiography data
using fully convolutional neural networks with 3D conditional random fields.
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Detection of Substances in the Left Atrial Appendage by Spatiotemporal Motion Analysis Based on 4D-CT.
Cheng Jin, Heng Yu,
Jianjiang Feng , Lei Wang,
Jiwen Lu,
Jie Zhou
MICCAI workshop, 2017 - Oral Presentation
paper
we present a new approach for the detection of substances in the left atrial appendage by spatiotemporal motion analysis and
make a detailed judgment and analysis of spatial distribution and classification of most objects in the left atrial appendage.
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Left atrial appendage neck modeling for closure surgery.
Cheng Jin, Heng Yu,
Jianjiang Feng , Lei Wang,
Jiwen Lu,
Jie Zhou
MICCAI workshop, 2017
paper
We propose a robust method for automatic left atrial appendage segmentation on computed tomographic angiography data
using fully convolutional neural networks with 3D conditional random fields.
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