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Sharon X. Huang |
Courses:
Research:
Selected Recent Publications: (See Google Scholar Profile for all publications) H. Ni, Y. Xue, L. Ma, Q. Zhang, X. Li, S.X. Huang, ``Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment," In Medical Image Analysis, Vol. 83, 2023. H. Ni, Y. Xue, K. Wong, J. Volpi, S. TC Wong, J.Z. Wang, X. Huang, ``Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT Scans," In Proc. Of International Conf. on Medical
Image Computing and Computer Assisted Intervention (MICCAI), 2022. [code] Y. Ou, Y. Yuan, X. Huang, S. TC Wong, J. Volpi, J.Z. Wang, K. Wong, ``Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation," In Proc. Of International Conf. on Medical
Image Computing and Computer Assisted Intervention (MICCAI), 2022. [code] J. Liu, Y. Xue, J. Duarte, K. Shekhawat, Z. Zhou, X. Huang, ``End-to-end Graph-constrained Vectorized Floorplan Generation with Panoptic Refinement," In Proc. of the European Conference on Computer Vision (ECCV), 2022. J. Ye, Y.-T. Yeh, Y. Xue, Z. Wang, N. Zhang, L. He, K. Zhang, R. Ricker, Z. Yu, A. Roder, N.P. Lopez, L. Organtini, W. Greene, S. Hafenstein, H. Lu, E. Ghedin, M. Terrones, S. Huang, S.X. Huang, ``Accurate virus identification with interpretable Raman signatures by machine learning," In The Proceedings of the National Academy of Sciences (PNAS), 2022. [code] T. Cai, H. Ni, M. Yu, X. Huang, K. Wong, J. Volpi, J. Z. Wang, S. TC Wong, ``DeepStroke: An Efficient Stroke Screening Framework for Emergency Rooms with Multimodal Adversarial Deep Learning," In Medical Image Analysis, Vol. 80, 2022. F. Yang, X. Huang, Z. Zhou, ``Deep Depth from Focus with Differential Focus Volume," In Proc. Of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [code] J. Liu, P. Ji, N. Bansal, C. Cai, Q. Yan, X. Huang, Y. Xu, ``PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo," In Proc. Of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. P. Jin, X. Zhang, Y. Chen, S.X. Huang, Z. Liu, Y. Lin, ``Unsupervised learning of full-waveform inversion: Connecting CNN and partial differential equation in a loop," In Proc. Of International Conference on Learning Representations (ICLR), 2022. J. Ye, Y. Xue, P. Liu, K.C. Cheng, R. Zaino, X. Huang, ``A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis," In Proc. Of International Conf. on Medical
Image Computing and Computer Assisted Intervention (MICCAI), 2021. [code] Y. Ou, Y. Yuan, X. Huang, K. Wong, J. Volpi, J. Z. Wang, S. T.C. Wong, ``LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images," In Proc. Of International Conf. on Medical
Image Computing and Computer Assisted Intervention (MICCAI), 2021. [code] Y. Xue, J. Ye, Q. Zhou, L.R. Long, S. Antani, Z. Xue, C. Cornwell, R. Zaino, K.C. Cheng, X. Huang, ``Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification," In Medical Image Analysis, 2021. Y. Xue, Y.C. Guo, H. Zhang, T. Xu, S.H. Zhang, X. Huang, ``Deep Image Synthesis from Intuitive User Input: A Review and Perspectives," In Computational Visual Media, 8(1):3-31, 2022. J. Ye, Y. Xue, L.R. Long, S. Antani, Z. Xue, K. Cheng, X. Huang, ``Synthetic Sample Selection via Reinforcement Learning," In Proc. Of International Conf. on Medical
Image Computing and Computer Assisted Intervention (MICCAI), 2020. H. Ni, Y. Xue, Q. Zhang, X. Huang, ``SiamParseNet: Joint Body Parsing and Label Propagation in Infant Movement Videos," In Proc. Of International Conf. on Medical
Image Computing and Computer Assisted Intervention (MICCAI), 2020. M. Yu, T. Cai, X. Huang, K. Wong, J. Volpi, J.Z. Wang, S. TC Wong, ``Toward Rapid Stroke Diagnosis with Multimodal Deep Learning," In Proc. Of International Conf. on Medical
Image Computing and Computer Assisted Intervention (MICCAI), 2020. Y. Xue, Z. Zhou, X. Huang, ``Neural Wireframe Renderer: Learning Wireframe to Image Translations," In Proc. of the European Conference on Computer Vision (ECCV), 2020. [code] S. Wang, Y. Zhou, X. Qin, S. Nair, X. Huang, Y. Liu, ``Label-free detection of rare circulating tumor cells by image analysis and machine learning," In Scientific Reports, 10(1), 2020. Y. Xue, H. Tang, Z. Qiao, G. Gong, Y. Yin, Z. Qian, C. Huang, W. Fan, X. Huang, ``Shape-Aware Organ Segmentation by Predicting Signed Distance Maps," In Proc. of 34th AAAI Conference on Artificial Intelligence (AAAI), 2020. Y. Xue, Q. Zhou, J. Ye, L.R. Long, S. Antani, C. Cornwell, Z. Xue, X. Huang, ``Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification, In Proc. Of International Conf. on Medical
Image Computing and Computer Assisted Intervention (MICCAI), pp. 387-396, 2019. Y. Xue, X. Huang, ``Improved Disease Classification in Chest X-rays with Transferred Features from Report Generation, In Proc. of International Conf. on Information Processing in Medical Imaging (IPMI), pp. 125-138, 2019. T. Xu, C. Langouras, M.A. Koudehi, B.E. Vos, N. Wang, G.H. Koenderink, X. Huang, D. Vavylonis, ``Automated Tracking of Biopolymer Growth and Network Deformation with TSOAX, In Scientific Reports, 9(1), p. 1717, 2019. [code] H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang,
X. Huang, D. Metaxas, ``StackGAN++: Realistic Image Synthesis with Stacked
Generative Adversarial Networks, In IEEE Trans. On Pattern Analysis and Machine Intelligence, 41(8), pp. 1947-1962, 2019. [code] Z. Shen, J. Wang, J. Jiang, S.X. Huang, Y. Lin, C. Nan, L. Chen, Y. Shen, ``Phase-field Modeling and Machine Learning of Electric-Thermal-Mechanical Breakdown of Polymer-based Dielectrics, In Nature Communications, 2019. Y. Ma, T. Xu, X. Huang, X. Wang, C. Li, J. Jerwick, Y. Ning, X. Zeng, B. Wang, Y. Wang, Z. Zhang, X. Zhang, C. Zhou, ``Computer-Aided Diagnosis of Label-Free 3D Optical Coherence Microscopy Images of Human Cervical Tissue, In IEEE Trans. on Biomedical Engineering, 2019. Y. Xue, T. Xu,
L.R. Long, Z. Xue, S. Antani,
G.R. Thoma, X. Huang, ``Multimodal Recurrent Model
with Attention for Automated Radiology Report Generation, In Proc. Of International Conf. on Medical
Image Computing and Computer Assisted Intervention (MICCAI), pp. 457-466, 2018. T. Xu, P. Zhang, Q. Huang, H. Zhang, Z. Gan, X. Huang, X. He, ``AttnGAN: Fine-Grained Text to Image Generation with
Attentional Generative Adversarial Networks, In Proc. Of IEEE Conf. on Computer Vision and Pattern Recognition,
2018. [code] Y. Xue, T. Xu, H.
Zhang, L. R. Long, and X. Huang, `` SegAN: Adversarial Network with Multi-scale L1 Loss for
Medical Image Segmentation, In Neuroinformatics, 16(3-4):383-392, 2018. [code] J. Yao, Z. Xu, X. Huang, J. Huang, ``An
Efficient Algorithm for Dynamic MRI using Low-rank and Total Variation
Regularizations," In Medical Image Analysis, Vol. 44, pp. 14-27, 2018. H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang,
X. Huang, D. Metaxas, ``StackGAN: Text to Photo-realistic Image Synthesis with
Stacked Generative Adversarial Networks," In Proc. of
International Conf. on Computer Vision, 2017. [code] S. Wan, H.C. Lee, X. Huang, T. Xu, T. Xu,
X. Zeng, Z. Zhang, Y. Sheikine, J.L. Connolly, J.G.
Fujimoto, C. Zhou, ``Integrated
local binary pattern texture features for classification of breast tissue
imaged by Optical Coherence Microscopy,'' In Medical Image
Analysis, Vol. 38, pp. 104-116, 2017. T. Xu, H. Zhang, C. Xin, E. Kim, L.R. Long,
Z. Xue, S. Antani, X.
Huang, ``Multi-feature
based benchmark for cervical dysplasia classification evaluation,'' In Pattern
Recognition, Vol. 63, pp. 468-475, 2017. T. Xu, H. Zhang, X. Huang , S. Zhang, D.
Metaxas, ``Multimodal
Deep Learning for Cervical Dysplasia Diagnosis,'' In Proc. of
International Conf. on Medical Image Computing and Computer Assisted
Intervention (MICCAI), LNCS Vol. 9901, pp. 115-123,
2016. H. Zhang, T. Xu, M. Elhoseiny,
X. Huang, S. Zhang, A. Elgammal, D. Metaxas, ``SPDA-CNN:
Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition,''
In Proc. of IEEE Computer Society Conf. on Computer Vision and
Pattern Recognition (CVPR), pp. 1143-1152, 2016. Z. Zhu, D. Liang, S. Zhang, X. Huang, B.
Li, S. Hu, ``Traffic-Sign
Detection and Classification in the Wild,'' In Proc. of IEEE
Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 2110-2118, 2016. T. Xu, D. Vavylonis,
F.C. Tsai, G.H. Koenderink, W. Nie,
E. Yusuf, I.J. Lee, J.Q. Wu, X. Huang, ``SOAX: A software for
quantification of 3D biopolymer networks," In Scientific Reports, 13;5:9081, 2015. [code] M. Cheng, N.J. Mitra,
X. Huang, P.H.S. Torr, S.M. Hu, ``Global
Contrast Based Salient Region Detection," In IEEE Trans. on
Pattern Analysis and Machine Intelligence (TPAMI),
37(3):569-82, 2015. D. Song, E. Kim, X. Huang, J. Patruno, H. Munoz-Avila, J. Heflin, L.R. Long, S. Antani, ``Multi-modal Entity Coreference for Cervical Dysplasia Diagnosis," In IEEE
Trans. on Medical Imaging (TMI), 34(1):229-45, 2015. H. Li, X. Huang, J. Huang, S. Zhang, ``Feature
Matching with Affine-Function Transformation Models," In IEEE
Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 36(12):2407-22, 2014. T. Xu, D. Vavylonis,
X. Huang, ``3D
Actin Network Centerline Extraction with Multiple Active Contours,'' In Medical
Image Analysis, 18(2):272-84, 2014. M. Cheng, N. J. Mitra,
X. Huang, S. M. Hu, ``SalientShape: Group Saliency in Image Collections,'' In
Visual Computer, August 2013. H. Li, X. Huang, L. He, ``Object Matching Using a Locally Affine Invariant and
Linear Programming Techniques,'' In IEEE Trans. on Pattern
Analysis and Machine Intelligence (TPAMI),
35(2):411-24, 2013. E. Kim, H. Li, X. Huang, ``A Hierarchical Image Clustering Cosegmentation
Framework,'' In Proc. of IEEE Computer Society Conf. on Computer
Vision and Pattern Recognition (CVPR), pp. 686-693,
2012. E. Kim, X. Huang, G. Tan, ``Markup SVG - An Online Content Aware Image Abstraction and
Annotation Tool,'' In IEEE Trans. on Multimedia (TMM), 13(5):993-1006, 2011. T. Shen, H. Li, X. Huang, ``Active Volume Models for Medical Image Segmentation,''
In IEEE Trans. on Medical Imaging (TMI), 30(3):774-791,
2011. H. Li, T. Shen, X. Huang, ``Approximately Global Optimization for Robust Alignment of
Generalized Shapes,'' In IEEE Trans. on Pattern Analysis and
Machine Intelligence(TPAMI), 33(6):1116-1131, 2011. E. Kim, X. Huang, J. Heflin, ``Finding VIPS - A Visual Image Persons Search Using a
Content Property Reasoner and Web Ontology,'' In Proc. of IEEE
International Conf. on Multimedia & Expo (ICME),
2011. M. Cheng, G. X. Zhang, N. J. Mitra, X. Huang, S. M. Hu, ``Global
Contrast based Salient Region Detection,'' In Proc. of IEEE
Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 409-416, 2011. M. B. Smith, H. Li, T. Shen, X. Huang, E.
Yusuf, D. Vavylonis, ``Segmentation and Tracking of
Cytoskeletal Filaments using Open Active Contours,'' In Cytoskeleton, 67(11): 693-705, 2010. M. Cheng, F. L. Zhang, N. J. Mitra, X. Huang, S. M. Hu, ``RepFinder: Finding
Approximately Repeated Scene Elements for Image Editing,'' In ACM
Trans. on Graphics, Vol. 29, No. 4, 2010. (Presented
at SIGGRAPH 2010) H. Li, E. Kim, X. Huang, L. He, ``Object Matching with a Locally Affine-Invariant
Constraint,'' In Proc. of IEEE Computer Society Conf. on
Computer Vision and Pattern Recognition (CVPR), pp.
1641-1648, 2010. J. Huang, X. Huang, D. Metaxas, ``Learning with Dynamic Group Sparsity,'' In Proc.
of IEEE International Conf. on Computer Vision (ICCV),
pp. 64-71, 2009. Yanglan Ou Haomiao Ni Amogh Adishesha Jiarong (Karen) Ye
Matthew Poska
Peng Jin
Jiachen Liu
Former
Ph.D. students: Fengting Yang -- Now Applied Research Scientist at Meta Reality Lab; co-advised with Z. Zhou Yuan Xue -- Now Postdoctoral Fellow at Johns Hopkins University Tao Xu -- Now Research Scientist at Meta (Facebook) Ting Xu -- Now Research Scientist at Konica Minolta Laboratory
Brief Bio:
Sharon Xiaolei
Huang received her B.E. degree in Computer Science from Tsinghua University,
China, and her M.S. and Ph.D. degrees in Computer Science from Rutgers
University. She is currently a Professor
in the College of Information Sciences and Technology and a member of the Huck Institutes of the Life Sciences at the Penn State
University, University Park, PA. Her research interests are in the areas of
biomedical image analysis, computer vision, and machine learning, focusing on methods for object recognition, image and video segmentation, image and video synthesis, computer-assisted diagnosis and intervention, registration/matching, and motion tracking. Her broader interests include AI for medicine, data science for healthcare, biomedical informatics, computer graphics, visualization, and human-computer interaction. She regularly serves on the program committees of major conferences in medical image computing and computer vision and is an associate editor for several journals including Medical Image Analysis, Computer Vision and Image Understanding, Computerized Medical Imaging and Graphics. In the College of IST, she currently serves as the program coordinator of Applied Data Sciences.
Fall 2022: Introduction to Data Sciences (DS 200)
Spring 2022: Algorithmic Methods and Tools (DS 305)
Fall 2021: Visual Analytics for Data Sciences (DS 330)
Spring 2021: Machine Learning in Healthcare (IST 597)
Fall 2020: Data Management for Data Sciences (DS 220)
Spring 2019: Machine Learning Methods in Biomedical Image Informatics (IST 597)
Fall 2018: Organization of Data (IST 210)
I am a computer scientist who is curious about how human brain, eye, body,
and cell, function. Hence I am always excited about developing robust image
analysis methods and building autonomous and intelligent systems that integrate
algorithms with efficient, application-specific designs to solve computational
problems in biomedicine, perception, and cognition. In particular, I work
toward robust medical imaging software based on computer vision and machine learning algorithms that aid medical doctors in accurate and
reproducible diagnosis, and help them better understand the anatomical and
physiological relationships in normal and diseased states. I develop image
processing and analysis software for analyzing biological images to help
biologists and biophysicists in understanding and modeling complex biological
pathways and systems. I also create intelligent vision systems that are capable
of learning effectively and reasoning about multiple sources of information in
order to achieve functions typical of human vision.
Current Ph.D. Students:
Edward Kim -- Now Associate
Professor at Drexel University
Hongsheng
Li -- Now Associate Professor at the Chinese University of Hong Kong
Yaoyao Zhu -- Now Senior Software Engineer at Checkpoint Systems
Tian Shen -- Now Principal Research Scientist at Tencent AI Lab