publication

underline: advised student
*: equal contribution

2025

Deep Learning-Based Pre-contrast CT Parcellation for MRI-Free Brain Amyloid PET Quantification 

#MRI   #CT   #Medical Imaging 

Kyobin Choo, Jaehoon Joo, Sangwon Kim, Daesung Kim, Hyunkeong Kim, Seongjin Kang, Seong Jae Hwang*, Mijin Yun*

Clinical Nuclear Medicine, 2024. [Impact Factor: 10.0]

Disentangling Disentangled Representations: Towards Improved Latent Units via Diffusion Models

#Disentanglement   #Diffusion Model 

Youngjun Jun, Jiwoo Park, Kyobin Choo, Tae Eun Choi, Seong Jae Hwang

Winter Conference on Applications of Computer Vision (WACV), 2025.

[arxiv]

DragText: Rethinking Text Embedding in Point-based Image Editing 

#Image Editing   #Diffusion Model 

Gayoon Choi, Taejin Jeong, Sujung Hong, Jaehoon Joo, Seong Jae Hwang

Winter Conference on Applications of Computer Vision (WACV), 2025. [Round 1 Accept (12% of round 1 submissions)]

[Project Page] [arxiv]

2024

EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation

#Object-centric Learning   #Segmentation  

Chanyoung Kim*, Woojung Han*, Dayun Ju, Seong Jae Hwang

Computer Vision and Pattern Recognition (CVPR), 2024. [Highlight (top 2.8% of submissions)]

[Project Page] [open access] [arxiv] [github]

Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning 

#Diffusion Model   #Text-to-Image Generation   #Reinforcement Learning   #Medical Imaging 

Woojung Han*, Chanyoung Kim*, Dayun Ju, Yumin Shim, Seong Jae Hwang

Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024. [Spotlight (top 3.4% of submissions)]

[Project Page] [arxiv] [github] 

Slice-Consistent 3D Volumetric Brain CT-to-MRI Translation with 2D Brownian Bridge Diffusion Model 

#Diffusion Model   #Image Translation   #Medical Imaging 

Kyobin Choo, Youngjun Jun, Mijin Yun, Seong Jae Hwang

Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024. [Early Accept (top 11% of submissions)]

[Project Page] [arxiv] [github]

Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstruction

#PEFT   #PET Reconstruction   #Medical Imaging 

Yumin Kim*, Gayoon Choi*, Seong Jae Hwang

Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024.

[Project Page] [arxiv] [github]

ESPA: An Unsupervised Harmonization Framework via Enhanced Structural Preserving Augmentation

#Data Harmonization   #Medical Imaging 

Mahbaneh Eshghzadeh Torbati, Davneet S. Minhas, Ahmad Tafti, Charles S. DeCarli, Dana L. Tudorascu, Seong Jae Hwang

Medical Image Computing and Computer Assisted Intervention (MICCAI), 2024.

[PDF]

Complementary Branch Fusing Class and Semantic Knowledge for Robust Weakly Supervised Semantic Segmentation 

#Semantic Segmentation   #Weakly Supervised Learning 

Woojung Han, Seil Kang, Kyobin Choo, Seong Jae Hwang

Pattern Recognition, 2024. [Impact Factor: 7.5]

[elsevier] [arxiv]

Superpixel-ComBat Modeling: A Joint Approach for Harmonization and Characterization of Inter-Scanner Variability in T1-Weighted Images

#Data Harmonization   #Medical Imaging 

Chang-Le Chen, Mahbaneh Eshaghzadeh Torbati, Davneet S. Minhas, Charles M. Laymon, Seong Jae Hwang, Murat Bilgel, Adina Crainiceanu, Hecheng Jin, Weiquan Luo, Pauline Maillard, Evan Fletcher, Ciprian M. Crainiceanu, Charles S. DeCarli, Howard J. Aizenstein, Dana L. Tudorascu

Imaging Neuroscience, 2024.

[MIT Press]

FALCON: Frequency Adjoint Link with CONtinuous Density Mask for Fast Single Image Dehazing

#Dehazing   #Image Restoration   

Donghyun Kim, Seil Kang, Seong Jae Hwang

arXiv, 2024.

[arxiv]

WoLF: Large Language Model Framework for CXR Understanding 

#LLM   #Vision-Language Model   #Medical Imaging 

Seil Kang, Donghyun Kim, Junhyeok Kim, Hyo Kyung Lee, Seong Jae Hwang

arXiv, 2024.

[arxiv]

Brain-Streams: fMRI-to-Image Reconstruction with Multi-modal Guidance

#Image Reconstruction   #Vision-Language Model   #Medical Imaging 

Jaehoon Joo, Taejin Jeong, Seong Jae Hwang

arXiv, 2024.

[arxiv]

PLATYPUS: Progressive Local Surface Estimator for Arbitrary-Scale Point Cloud Upsampling

#Point Cloud Upsampling 

Donghyun Kim, Hyeonkyeong Kwon, Yumin Kim, Seong Jae Hwang

arXiv, 2024.

[arxiv]

2023

MISPEL: A supervised deep learning method for harmonizing multi-scanner neuroimaging data

#Data Harmonization   #Medical Imaging  

Mahbaneh Eshghzadeh Torbati, Davneet S. Minhas, Charles M. Laymon, Pauline Maillard, James D. Wilson, Chang-Le Chen, Ciprian M. Crainiceanu, Charles S. DeCarli, Seong Jae Hwang, Dana L. Tudorascu

Medical Image Analysis (MIA), 2023. [Impact Factor: 13.828, JCR 1.2%]

[MIA]

Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improve

#Domain Generalization 

Anthony Sicilia, Xingchen Zhao, Seong Jae Hwang

Machine Learning, 2023. [Impact Factor: 7.5]

[Machine Learning][arxiv]

Evidence-empowered Transfer Learning for Alzheimer's Disease

#Transfer Learning   #Medical Imaging  

Kai Ong, Hana Kim, Minjin Kim, Jinseong Jang, Beomseok Sohn, Yoon Seong Choi, Dosik Hwang, Seong Jae Hwang, Jinyoung Yeo

IEEE International Symposium on Biomedical Imaging (ISBI), 2023.

[arxiv]

2022

PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners

#PAC-Bayesian Learning   #Domain Adaptation  

Anthony Sicilia, Katherine Atwell, Malihe Alikhani, Seong Jae Hwang

Conference on Uncertainty in Artificial Intelligence (UAI), 2022. [Best Paper Award (1 of 712 submitted papers)]

[UAI][arxiv][github]

Test-time Fourier Style Calibration for Domain Generalization

#Domain Generalization 

Xingchen Zhao, Chang Liu, Anthony Sicilia, Seong Jae Hwang, Yun Fu

International Joint Conference on Artificial Intelligence (IJCAI), 2022.

[IJCAI][arxiv]

The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error

#NLP   #Domain Adaptation  

Katherine Atwell, Anthony Sicilia, Seong Jae Hwang, Malihe Alikhani

Findings of ACL, 2022.

[Findings][arxiv][github]

Anatomic development of the upper airway during the first five years of life: A three-dimensional imaging study

#Medical Imaging  

Ying Ji Chuang, Seong Jae Hwang, Kevin A Buhr, Courtney A Miller, Gregory D Avey, Brad H Story, Houri K. Vorperian

PLOS ONE, 2022.

[PLOS ONE]

2021

A multi-scanner neuroimaging data harmonization using RAVEL and ComBat

#Data Harmonization   #Medical Imaging  

Mahbaneh Eshghzadeh Torbati, Davneet S. Minhas, Ghasan Ahmad, Erin E. O'Connor, John Muschelli, Charles Laymon, Zixi Yang, Annie Cohen, Howard J. Aizenstein, William E. Klunk, Bradley T. Christian, Seong Jae Hwang, Ciprian M. Crainiceanu, Dana L. Tudorascu

NeuroImage, 2021.

[NeuroImage]

Multi-scanner Harmonization of Paired Neuroimaging Data via Structure Preserving Embedding Learning

#Data Harmonization   #Medical Imaging  

Mahbaneh Eshghzadeh Torbati, Dana L. Tudorascu, Pauline Maillard, Davneet S. Minhas, Charles S. DeCarli, Seong Jae Hwang

The First Workshop on Computer Vision for Automated Medical Diagnosis (CVAMD), International Conference on Computer Vision (W-ICCV), 2021. [Oral]

[W-ICCV] [github]

Point Cloud Augmentation with Weighted Local Transformations

#Point Cloud 

Sihyeon Kim, Sanghyeok Lee, Dasol Hwang, Jaewon Lee, Seong Jae Hwang, Hyunwoo Kim

International Conference on Computer Vision (ICCV), 2021.

[ICCV] [github]

PAC Bayesian Performance Guarantees for Deep (Stochastic) Networks in Medical Imaging

#PAC-Bayesian Learning   #Medical Imaging  

Anthony Sicilia, Xingchen Zhao, Anastasia Sosnovskikh, Seong Jae Hwang

Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021.

[MICCAI] [arxiv] [github]

Cycle Consistent Embedding of 3D Brains with Auto-Encoding Generative Adversarial Networks

#GAN   #Medical Imaging  

Shibo Xing, Harsh Sinha, Seong Jae Hwang

Medical Imaging with Deep Learning (Short Paper - MIDL), 2021.

[openreview] [github]

Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders

#GCN   #Medical Imaging  

Xin Ma, Guorong Wu, Seong Jae Hwang, Won Hwa Kim

Information Processing in Medical Imaging (IPMI), 2021. [Acceptance rate: 30.0%]

[IPMI]

Multi-Domain Learning by Meta-Learning: Taking Optimal Steps in Multi-Domain Loss Landscapes by Inner-Loop Learning

#Meta-learning   #Medical Imaging  

Anthony Sicilia, Xingchen Zhao, Davneet Minhas, Erin O'Connor, Howard Aizenstein, William Klunk, Dana Tudorascu, Seong Jae Hwang

IEEE International Symposium on Biomedical Imaging (ISBI), 2021.

[ISBI 2021] [arxiv] [github]

Robust White Matter Hyperintensity Segmentation on Unseen Domain

#Domain Generalization   #Segmentation  #Medical Imaging  

Xingchen Zhao, Anthony Sicilia, Davneet Minhas, Erin O'Connor, Howard Aizenstein, William Klunk, Dana Tudorascu, Seong Jae Hwang

IEEE International Symposium on Biomedical Imaging (ISBI), 2021.

[ISBI 2021] [arxiv] [github]

2020

Developmental morphology of the cervical vertebrae and the emergence of sexual dimorphism in size and shape: A computed tomography study

#Medical Imaging  

Courtney A. Miller, Seong Jae Hwang, Meghan M. Cotter, Houri K. Vorperian

The Anatomical Record, 2020.

[The Anatomical Record]

Classifying Nuclei Shape Heterogeneity in Breast Tumors with Skeletons

#Medical Imaging  

Brian Faulkenstein, Adriana Kovashka, Seong Jae Hwang, S. Chakra Chennubhotla

Workshop on BioImage Computing, European Conference on Computer Vision (ECCV Workshop), 2020. [Oral Presentation]

[Springer link] [openreview pdf]

Learning Amyloid Pathology Progression from Longitudinal PiB-PET Images in Preclinical Alzheimer's Disease

#Medical Imaging  

Wei Hao, Nicholas M. Vogt, Zihang Meng, Seong Jae Hwang, Rebecca L. Koscik, Sterling C. Johnson, Barbara B. Bendlin, Vikas Singh

IEEE International Symposium on Biomedical Imaging (ISBI), 2020.

[IEEE Xplore]

2019

Statistical Analysis of Longitudinally and Conditionally Generated Neuroimaging Measures via Conditional Recurrent Flow

#Normalizing Flow   #Medical Imaging  

Seong Jae Hwang, Zirui Tao, Won Hwa Kim, Vikas Singh

The First Workshop on Statistical Deep Learning in Computer Vision, International Conference on Computer Vision (W-ICCV), 2019. [Oral]

[pdf]

Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging

#Normalizing Flow   #Medical Imaging  

Seong Jae Hwang, Zirui Tao, Won Hwa Kim, Vikas Singh

International Conference on Computer Vision (ICCV), 2019. [Acceptance rate: 25%]

[pdf] [poster]

Sampling-free Uncertainty Estimation in Gated Recurrent Units with Applications to Normative Modeling in Neuroimaging

#Uncertainty   #Medical Imaging  

Seong Jae Hwang, Ronak Mehta, Hyunwoo J. Kim, Sterling C. Johnson, Vikas Singh

Conference on Uncertainty in Artificial Intelligence (UAI), 2019. [Acceptance rate: 26%]

[pdf]

Large-Scale Training Framework for Video Annotation

#Video Understanding  

Seong Jae Hwang, Joonseok Lee, Balakrishnan Varadarajan, Ariel Gordon, Zheng Xu, Paul Natsev

Conference on Knowledge Discovery and Data Mining (KDD), 2019. [Oral acceptance rate: 9.2%] [Oral]

[pdf]

Cervical vertebral growth and emergence of sexual dimorphism: A developmental study using computed tomography

#Medical Imaging  

Courtney A. Miller, Seong Jae Hwang, Meghan M. Cotter, Houri K. Vorperian

Journal of Anatomy, 2019.

2018

Cerebrospinal fluid biomarkers of neurofibrillary tangles and synaptic dysfunction are associated with longitudinal decline in white matter connectivity: A multi-resolution graph analysis

Won Hwa Kim, Annie M. Racine, Nagesh Adluru, Seong Jae Hwang, Kaj Blennow, Henrik Zetterberg, Cyhthia M. Carlsson, Sanjay Asthana, Rebecca L. Koscik, Sterling C. Johnson, Barbara B.Bendlin, Vikas Singh

NeuroImage: Clinical, 2018.

[NeuroImage: Clinical]

Associations between PET Amyloid Pathology and DTI Brain Connectivity in Preclinical Alzheimer's Disease

Seong Jae Hwang, Nagesh Adluru, Won Hwa Kim, Sterling C. Johnson, Barbara B. Bendlin, Vikas Singh

Brain Connectivity, 2018.

[Brain Connectivity] 

Tensorize, Factorize and Regularize: Robust Visual Relationship Learning

Seong Jae Hwang, Sathya N. Ravi, Zirui Tao, Hyunwoo J. Kim, Maxwell D. Collins, Vikas Singh

Computer Vision and Pattern Recognition (CVPR), 2018. [Acceptance rate: 29.7%]

[pdf]

2017

Online Graph Completion: Multivariate Signal Recovery in Computer Vision

Won Hwa Kim, Mona Jalal, Seong Jae Hwang, Sterling C. Johnson, Vikas Singh

Computer Vision and Pattern Recognition (CVPR), 2017.

[pdf]

2016

Adaptive Signal Recovery on Graphs via Harmonic Analysis for Experimental Design in Neuroimaging

Won Hwa Kim, Seong Jae Hwang, Nagesh Adluru, Sterling C. Johnson, Vikas Singh

European Conference on Computer Vision (ECCV), 2016. [Acceptance rate: 26.6%]

[pdf] [supplementary]

Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks

Seong Jae Hwang, Nagesh Adluru, Maxwell D. Collins, Sathya N. Ravi, Barbara B. Bendlin, Sterling C. Johnson, Vikas Singh

Computer Vision and Pattern Recognition (CVPR), 2016. [Acceptance rate: 29.9%]

[pdf] [supplementary] [poster] [code will be available here] [project page]

2015

A Projection free method for Generalized Eigenvalue Problem with a nonsmooth Regularizer

Seong Jae Hwang, Maxwell D. Collins, Sathya N. Ravi, Vamsi K. Ithapu, Nagesh Adluru, Sterling C. Johnson, Vikas Singh

International Conference on Computer Vision (ICCV), 2015. [Acceptance rate: 30.9%]

[pdf] [fixed eq (18),(20)] [supplementary] [poster] [code] [project page] [PubMed]

2022

Reducing MRI Inter-Scanner Variability Using 3D Superpixel ComBat

Chang-Le Chen, Mahbaneh Eshaghzadeh Torbati, James D. Wilson, Davneet S. Minhas, Charles M. Laymon, Seong Jae Hwang, Pauline Maillard, Evan Fletcher, Charles S. DeCarli, Dana L. Tudorascu

Alzheimer's Association International Conference (AAIC), 2022.

2021

An MRI multi-scanner neuroimaging data harmonization study using RAVEL and ComBat

Mahbaneh Eshghzadeh Torbati, Davneet S. Minhas, Ghasan Ahmad, Erin E. O'Connor, John Muschelli, Charles Laymon, Zixi Yang, Annie Cohen, Howard J. Aizenstein, William E. Klunk, Bradley T. Christian, Ciprian M. Crainiceanu, Seong Jae Hwang, Dana L. Tudorascu

Alzheimer's Association International Conference (AAIC), 2021.

2020

Retrospective prediction of amyloid accumulation trajectories in a risk-enriched Alzheimer's disease cohort with sequential neural networks

Seong Jae Hwang, Rebecca L. Koscik, Tobey J. Betthauser, Zirui Tao, Won Hwa Kim, Sterling C. Johnson, Vikas Singh

Human Amyloid Imaging (HAI), 2020. [poster]

2019

Predicting amyloid accumulation trajectories in a risk-enriched Alzheimer's disease cohort with Deep Conditional Neural Networks

Seong Jae Hwang, Rebecca L. Koscik, Tobey J. Betthauser, Zirui Tao, Won Hwa Kim, Sterling C. Johnson, Vikas Singh

Alzheimer's Association International Conference (AAIC), 2019.

A Normative Modeling Based Analysis of Amyloid, Cognition, and Tau in Preclinical Alzheimer's Disease

Zirui Tao, Ronak R. Mehta, Seong Jae Hwang, Rebecca L. Koscik, Erin Jonaitis, Sterling C. Johnson, Vikas Singh

Alzheimer's Association International Conference (AAIC), 2019.

Sequential Deep Learning Algorithms show structural connectivity differences by amyloid status

Xingjian Zhen, Rudrasis Chakraborty, Nocholas Vogt, Seong Jae Hwang, Sterling C. Johnson, Barbara B. Bendlin, Vikas Singh

Alzheimer's Association International Conference (AAIC), 2019.

2018

Data-Driven Propagation Modeling of PET-Derived Alzheimer's Pathology in a Preclinical Cohort

Seong Jae Hwang, Sathya N. Ravi, Nagesh Adluru, Barbara B. Bendlin, Sterling C. Johnson, Vikas Singh

Alzheimer's Association International Conference (AAIC), 2018.

2017

Graph Completion: A Generalization of Netflix Prize Problem to Designing Cost Effective Neuroimaging Trials in Preclinical AD

Won Hwa Kim, Seong Jae Hwang, Nagesh Adluru, Sterling C. Johnson, Vikas Singh

Alzheimer's Association International Conference (AAIC), 2017.

2016

Multi-Resolution Analysis of DTI-Derived Brain Connectivity and the Influence of PET-Derived Alzheimer's Disease Pathology in a Preclinical Cohort

Seong Jae Hwang, Won Hwa Kim, Barbara B. Bendlin, Nagesh Adluru, Vikas Singh

Alzheimer's Association International Conference (AAIC), 2016. [Oral]

Framework for Training Machine-Learned Models on Extremely Large Datasets

Joon Seok Lee, Balakrishnan Varadarajan, Ariel Gordon, Apostol Ivanov Natsev, Seong Jae Hwang

US Patent, 2021, US20210117728A1

[Google Patents]

Neural Network Architecture with Concurrent Uncertainty Output

Seong Jae Hwang, Ronak Mehta, Vikas Singh

US Patent, 2020, US20200065648A1

[Google Patents]

Computerized System for Efficient Augmentation of Data Sets

Won Hwa Kim, Seong Jae Hwang, Nagesh Adluru, Sterling Johnson, Vikas Singh

US Patent, 2018, US20180113990A1

[Google Patents]

Fast object tracking framework for sports video recognition

Zheng Han, Xiaowei Dai, Seong Jae Hwang, Jason Fass

US Patent, 2016, US9449230B2

[Google Patents]