Research Fields
My research focuses span across machine learning, deep learning, and generative artificial
intelligence:
- Computer Vision: Medical image analysis, image registration/segmentation, ultrasound
reconstruction
- Recommendation Systems: User modeling, video/audio representation learning
- Natural Language Processing: Large language models, retrival-augmented generation
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SMPL-A: Modeling Person-Specific Deformable Anatomy
Hengtao Guo,
Benjamin Planche,
Meng Zheng,
Srikrishna Karanam,
Terrence Chen,
Ziyan Wu
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
We propose a learning-based method that uses medical scans to predict internal organ deformation
across various human poses, aiding radiotherapy and similar treatments. This approach builds a
patient-specific model encoding the organ's shape and elasticity, allowing for deformation
estimation based on the patient's current pose. This innovation offers clinicians precise guidance
without additional scans or procedures.
poster
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Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual
Learning
Hengtao Guo,
Sheng Xu,
Bradford J. Wood,
Pingkun Yan
Medical Image Computing and Computer Assisted Intervention (MICCAI), 2020
We propose a deep contextual learning network (DCL-Net), which can efficiently exploit the image
feature relationship between US frames and reconstruct 3D US volumes without any tracking device.
The proposed DCL-Net utilizes 3D convolutions over a US video segment for feature extraction. An
embedded self-attention module makes the network focus on the speckle-rich areas for better spatial
movement prediction, with a novel case-wise correlation loss to stabilize the training
process for improved accuracy.
arxiv
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code
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Ultrasound Volume Reconstruction From Freehand Scans Without
Tracking
Hengtao Guo,
Hanqing Chao,
Sheng Xu,
Bradford J. Wood,
Jing Wang,
Pingkun Yan
IEEE Transactions on Biomedical Engineering, 2022
We propose a deep contextual-contrastive network, utilizing self-attention to focus on the
speckle-rich areas to estimate spatial movement and then minimizes a margin ranking loss for
contrastive feature learning. We train and validate the model on two independent datasets, evaluated
with including the DICE of reconstructed organ segmentation.
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Knowledge-based Analysis for Mortality Prediction from CT Images
Hengtao Guo,
Uwe Kruger,
Ge Wang,
Mannudeep K. Kalra,
Pingkun Yan
IEEE Journal of Biomedical and Health Informatics, 2019
This paper introduces a knowledge-based analytical method using deep convolutional neural network
(CNN) for all-cause mortality prediction. The underlying approach combines structural image features
extracted from CNNs, based on LDCT volume at different scales, and clinical knowledge obtained from
quantitative measurements, to predict the mortality risk of lung cancer screening subjects.
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Ultrasound Frame-to-Volume Registration via Deep Learning for
Interventional Guidance
Hengtao Guo,
Xuanang Xu,
Xinrui Song,
Sheng Xu,
Hanqing Chao,
Joshua Myers,
Baris Turkbey,
Peter A. Pinto,
Bradford J. Wood,
Pingkun Yan
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2023
We propose a novel US frame-to-volume registration (FVReg) pipeline to bridge the dimensionality gap
between 2-D US frames and 3-D US volume. The developed pipeline is implemented using deep neural
networks, which are fully automatic without requiring external tracking devices. We validated our
method on a clinical dataset with 618 subjects and tested its potential on real-time 2-D-US to
3-D-MR fusion navigation tasks. The proposed FVReg achieved an average target navigation error of
1.93 mm at 5 to 14 fps.
code
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Deep learning predicts cardiovascular disease risks from lung cancer
screening low dose computed tomography
Hanqing Chao,
Hongming Shan,
Fatemeh Homayounieh,
Ramandeep Singh,
Ruhani Doda Khera,
Hengtao Guo,
Timothy Su,
Ge Wang,
Mannudeep K. Kalra,
Pingkun Yan
Nature Communications, 2021
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general
population. Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for
simultaneous CVD risk estimation in at-risk patients. Our deep learning CVD risk prediction model,
trained with 30,286 LDCTs from the National Lung Cancer Screening Trial, achieves an area under the
curve (AUC) of 0.871 on a separate test set of 2,085 subjects and identifies patients with high CVD
mortality risks (AUC of 0.768).
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Review Service
- Computer Vision and Pattern Recognition (CVPR)
- European Conference on Computer Vision (ECCV)
- Medical Image Computing and Computer Assisted Intervention (MICCAI)
- Machine Learning in Medical Imaging
- Computerized Medical Imaging and Graphics
- Artificial Intelligence with Biased or Scarce Data
- Frontiers in Cardiovascular Medicine
- Neurocomputing
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