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Xiaoling HuPostdoctoral Research FellowLaboratories for Computational Neuroimaging (LCN)Athinoula A. Martinos Center for Biomedical ImagingMGH/Harvard Medical SchoolResearch Statement / CV / Linkedin / Twitter / Scholar / Github |
I am currently a Postdoctoral Research Fellow at MGH/Harvard Medical School, hosted by Prof. Juan Eugenio Iglesias and Prof. Bruce Fischl.
I obtained my PhD degree from the Department of Computer Science at Stony Brook University, advised by Prof. Chao Chen, and worked closely with Prof. Dimitris Samaras and Prof. Prateek Prasanna. During my PhD, I have also been fortunate to spend time at United Imaging Intelligence (UII) and Allen Institute for Cell Science to explore various biomedical scenarios/applications.
Before that, I obtained my master and bachelor degrees from Tsinghua University and Huazhong University of Science and Technology, respectively.
Research Interests:
My research interest is Biomedical AI, and I am focusing on developing core AI/ML algorithms applied to medical imaging problems.
In particular, I am interested in:
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- Topology-Driven Deep Image Analysis [NeurIPS'19, ISBI'21, ICLR'21, MICCAI'21, ICLR'22, ECCV'22, arXiv'22, NeurIPS'22, ICLR'23b, NeurIPS'23, Dissertation, arXiv'23a]
- Uncertainty Estimation and Its Applications [ICLR'23a, ICLR'23b, ICCV'23b, NeurIPS'23]
- Learning with Imperfect Data [ICLR'23a, ICCV'23a, ICCV'23b, arXiv'23a, MedIA'24]
- Brain Image Analysis [arXiv'23b, arXiv'24a, arXiv'24b]
Contact Address: 149 13th St, Charlestown, MA 02129
Tutorials/Workshops
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The First Workshop on Topology- and Graph-Informed Imaging Informatics (TGI3) |
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Topology-Driven Image Analysis |
Selected Publications (Full Publications)
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Registration by Regression (RbR): a framework for interpretable and flexible atlas registration |
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P-Count: Persistence-based Counting of White Matter Hyperintensities in Brain MRI |
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TopoSemiSeg: Enforcing Topological Consistency for Semi-Supervised Segmentation of Histopathology Images |
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Brain-ID: Learning Robust Feature Representations for Brain Imaging |
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Anomaly-guided weakly supervised lesion segmentation on retinal OCT images |
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Topology-Aware Uncertainty for Image Segmentation |
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Learning Topological Representations for Deep Image Understanding |
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Calibrating Uncertainty for Semi-Supervised Crowd Counting |
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Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation |
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Learning Probabilistic Topological Representations Using Discrete Morse Theory |
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Confidence Estimation Using Unlabeled Data |
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Structure-Aware Image Segmentation with Homotopy Warping |
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Deep Statistic Shape Model for Myocardium Segmentation |
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Learning Topological Interactions for Multi-Class Medical Image Segmentation |
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Trigger Hunting with a Topological Prior for Trojan Detection |
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A Topology-Attention ConvLSTM Network and Its Application to EM Images |
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Topology-aware Segmentation Using Discrete Morse Theory |
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3D Topology-Preserving Segmentation with Compound Multi-Slice Representation |
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Topology-Preserving Deep Image Segmentation |
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Saliency detection based on integration of central bias, reweighting and multi-scale for superpixels |
Awards
Industry Experience
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Topology-Aware Image Segmentation |
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Deep Shape Model Based Network |
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Object Detection and Clothes Search |
Services
Reviewer for ICML, ICLR, NeurIPS, CVPR, ICCV, ECCV, AISTATS, MICCAI, AAAI etc.