Kayhan Batmanghelich
Assistant Professor
Department of Electrical and Computer Engineering
Boston University
8 St Mary's St, Office 421
Boston, MA 02215
batman@bu.edu
Phone: 617-358-0538
Brief Bio
I am an Assistant Professor in the Department of Electrical and Computer Engineering at Boston University. My research focuses on medical image analysis and the broader application of artificial intelligence in healthcare. Previously, I was a faculty member in the Department of Biomedical Informatics at the University of Pittsburgh. I received my PhD from the University of Pennsylvania. I am a recipient of the NSF CAREER Award and a Google Academic Research Award.
Research
My lab studies the fundamental challenges of AI in healthcare: (1) explainability, (2) data efficiency, (3) multimodal data fusion, and (4) causality. We develop machine learning methods for complex clinical problems, with applications in Breast Cancer, Chronic Obstructive Pulmonary Disease (COPD), and Alzheimer's disease. Our research is supported by funding from the NIH, NSF, and industry partners. I am also a co-founder of MLxMed, a multi-campus online seminar series focused on machine learning methods for healthcare, and READE.ai, a startup developing real-time evaluation of adverse events during surgery.
Students and Postdocs
Batman Lab Alumni
News
2025
- [Jul 2025]I am honored to have received the NSF CAREER award!
- [May 2025]We are excited that NIH awarded us $3.1M to continue developing AI technology to study lung COPD.
- [May 2025]Congratulations to Shantanu! His paper, Ladder, was accepted at ACL 2025.
- [Jan 2025]Congratulations to Chenyu and Wenchao! Our paper has been accepted to NAACL!
2024
- [Sep 2024]Google Academic Research Award! Our collaborative project with Dr. Eslami and Dr. Poynton has received a Google Academic Research Award !
- [Aug 2024]Our method to identify the disease change is out in Radiology AI! RSNA Radiology AI journal accepted our paper about disease change identification using an anatomically informed approach. Congrats to Ke and the rest of the team!
- [Jun 2024]MedSyn paper is accepted to TMI! Congratulations to Yanwu and Li ! MedSyn is the first prompable 3D diffusion model of lung CT!
- [May 2024]Our lab received Hariri Focus Research Award! Our collaborative project with Dr. Clare Poynton to develop a Vision Language model to audit risk models for breast cancer has received Hariri Focused Research awards!
- [May 2024]Early accept of Mammo-CLIP in MICCAI! Vision Language Foundational model for joint embedding of mammogram image and radiology reports is early accepted to MICCAI, congrats to Shantanu !
2023
- [Dec 2023]Self-Supervised Learning paper is in MedIA! Congrats to Ke and Li for their recent paper in MedIA ! This is a way to go for Self-Supervised Learning in medical imaging .
- [Sep 2023]Our fast diffusion paper is accepted in NeurIPs! Congrats to Yanwu for his second NeurIPs paper ! We are going to have more development in this direction soon!
- [Sep 2023]Our paper about shortcut learning is accepted to TMLR! Congratulations to Niahl for his first journal paper in TMLR ! The pre-print is available here and the final camera-ready, code and video will be out soon!
- [Jul 2023]The harmonization paper is accepted to NeuroImage Clinical! Congratulation to Max Reynolds for his first journal in NeuroImage Clinical Journal! The paper revisits the COMBAT method from a fully Bayesian point of view.
- [Jun 2023]Two early acceptances in MICCAI 2023! Congratulations to Shantanu and Matthew for their first papers in MICCAI, both early accept! I am very proud of them. Links to the paper and code are coming out soon.
- [Jun 2023]Imaging-Transcriptomics paper is accept to the COPD journal! We use DL techniques to define new COPD axes using CT imaging and gene expression data. Congratulation to Junxiang for his paper in the COPD Journal ! The pre-print is here !
- [Apr 2023]One paper is accepted to ICML 23! Congratulations to Shantanu and Ke ! Their first paper is accepted to ICML 2023. Find the paper, code, and more on the project page .
2022
- [Oct 2022]One paper is accepted to WACV! Congrats to Sumedha and Nihal for their paper in WACV ! They showed how our previous work on Counterfactual Explainer could be used to fix an overconfident BlackBox classifier!
- [Sep 2022]Yingci's manuscript is accepted to the oral oncology! Happy for Yingci! Her manuscript has been accepted to Oral Oncology!
- [Sep 2022]Counterfactual blackbox explanation paper is accepted to MedIA! Congratulation to Sumedha and her team! Her manuscript on counterfactual model explanation paper is finally accepted for a special issue in MedIA about XAI! The very initial pre-print is here; the full version is coming out soon! I am thankful to Motahare for her amazing contribution to the paper.
- [Jun 2022]Two papers are accepted in MICCAI 2022! Two papers are accepted in MICCAI 2022 ! Congrats to Ke Yu, Yanwu Xu , and Shantanu Ghosh !
- [Apr 2022]An efficient 3D GANs is finally out! Congratulations to Li and his team for their big paper in IEEE JBHI that makes volumetric GANs possible for high-resolution medical images. They made a huge effort, and I am proud of them!
- [Mar 2022]Yanwu's Paper about Adversarial Spatial Perturbation is accepted to CVPR! Congratulations to Yanwu for his CVPR paper! His method uses the Maximal Spatial Perturbation idea that significantly enhances image-to-image translation!
2021
- [Dec 2021]Our paper about Knowledge Distillation is accepted to AAAI 21! Congratulations to Ardavan, Li ! Their paper is accepted to AAAI 21! The link and code will be posted soon.
- [Sep 2021]Our collaborative work with MIT is accepted to NeurIPS! Our collaborative work with Suvrit's group about shortcuts in Self-supervised Learning is accepted to NeurIPS! Congratulations to Joshua , Li , and Ke !
- [Sep 2021]Our paper is accepted to Radiology AI! Congratulations to Brian ! His paper about estimating liver elastography is accepted to the Radiology AI journal!
- [Jun 2021]Two papers are accepted to MICCAI 2021! Two papers are accepted in the MICCAI 2021! Congratulations to Sumedha and Rohit !
- [Jun 2021]One paper is accepted to MLHC 2021! Congrats to Ardavan and Sumedha! Their paper is accepted to the Machine Learning in Healthcare, MLHC 2021 !
- [May 2021]Invited for Senior Vice Chancellor's Research Seminar! I'm very honored to give Senior Vice Chancellor's Research Seminar today. I'll present research done by brilliant students and postdocs at BatmanLab!
- [May 2021]One Early Acceptance to MICCAI! I am excited for Rohit Jena ! His paper received Early Acceptance in MICCAI 2021 ! Pre-print is here !
2020
- [Dec 2020]Our paper is accepted to Medical Physics Journal! I am happy for Sumedha ! Her first journal is accepted to Medical Physics !
- [Dec 2020]One paper is accepted to AAAI 2021! Congratulations to Li Sun and Ke Yu for their joint paper in AAAI 2021! Their paper show how to incorporate anatomically relevant context to self-supervised learning!
- [Oct 2020]Ke's journal is accepted to ACM JCIM! Congratulations to Ke Yu ! His paper is accepted to the Journal of Chemical Information and Modeling . The method integrates drug taxonomy with chemical structure and enables localizing novel molecules in the context of the clinically approved drugs.
- [Oct 2020]Brian and Stephen won an award for the liver project! Congratulations to Brian ! His collaborative work with Stephen Cai and Amir Borhani on estimating liver stiffness using a machine learning method received Cum Laude Award from the 43rd Society of Body Imaging Conference .
- [Sep 2020]Our paper is accepted to the Oxford Bioinformatics Journal! Congratulations to Mingming Gong ! His awesome paper ( Preprint ) is accepted to the Oxford Bioinformatics Journal ! Fundamental and rigorous research for inference of statical independence which uses unpaired data!
- [Aug 2020]Giving talk at the Oxford ML Summer School! I am excited to present at the Oxford ML Summer School ( OxML 2020 )! I will talk about various applications and challenges of Machine Learning in Medical Imaging!
- [Jun 2020]One paper is accepted to ICML 2020! Our paper ( Label-Noise Robust Domain Adaptation ) is accepted to ICML 2020! Congrats to Xiyu Yu and the team!
- [Jan 2020]Giving a talk in DeepMind about XAI for Healthcare! Excited to give a talk in DeepMind about Real-World Applications of Explainable Models in Medical Imaging!
2019
- [Dec 2019]One paper is accepted to ICLR as a spotlight! Our paper ( Explanation by Progressive Exaggeration ) is accepted as a Spotlight paper to ICLR 2020 ! Congrats to Sumedha !
- [Nov 2019]Two papers are accepted to the AAAI! Two papers ( #1 , #2 ) are accepted to AAAI 2020 ! Big congrats to Junxiang Chen , Yanwu Xu , and Mingming Gong !
- [Oct 2019]Giving a talk at SAP Machine Learning Retreat! Excited to give a talk about our recent NeurIPS paper at SAP Research Retreat !
- [Sep 2019]Our paper is accepted to NeurIPs as a spotlight paper! Our manuscript is accepted to NeurIPS 2019 (Spotlight 2.4%)! Big congrats to Mingming and Yanwu ! The code is in this repo .
- [Feb 2019]Mingming will be Lecturer at Stat Department in Melbourne University! First BatmanLab alumni! Congratulation to Mingming for accepting a new position as a lecturer (Assistant Professor) at the School of Mathematics and Statistics at the University of Melbourne!
2018
- [Sep 2018]Our collaborative proposal with Suvrit Sra (MIT) received $600K from the NSF Division of Mathematical Sciences !
- [Jun 2018]Mingming 's team won the single image depth prediction competition in Robust Vision Challenge 2018 !
- [May 2018]We are awarded a large R01 ($2.8M with indirect) to develop an approach to integrate Radiomic data with Genetic for characterization of Chronic Obstructive Pulmonary Disease (COPD).
- [Apr 2018]Congratulations to Sumedha for the Early Acceptance of her first paper to MICCAI !
- [Apr 2018]We are awarded $390K to develop methods for multimodal learning in collaboration with SAP research .
- [Feb 2018]Congratulations to Mingming Gong –two CVPR papers have been accepted!
2017
- [May 2017]Congrats to Yashin ! A coalition of the BatmanLab (ourLab) and MedGIFT won the tuberculosis Multi-drug resistance competition .
Publications
2025
Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation
@inproceedings{wang2025semantic,
title={Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation},
author={Wang, Chenyu and Zhou, Weichao and Ghosh, Shantanu and Batmanghelich, Kayhan and Li, Wenchao},
booktitle={Findings of the Association for Computational Linguistics: NAACL 2025},
year={2025},
url={https://arxiv.org/abs/2412.04606}
}
A Human-Centered Approach to Identifying Promises, Risks, \& Challenges of Text-to-Image Generative AI in Radiology
@inproceedings{morrison2025human,
title={A Human-centered approach to identifying promises, risks, \& challenges of text-to-image generative AI in radiology},
author={Morrison, Katelyn and Mathur, Arpit and Bradshaw, Aidan and Wartmann, Tom and Lundi, Steven and Zandifar, Afrooz and Dai, Weichang and Batmanghelich, Kayhan and Eslami, Motahhare and Perer, Adam},
booktitle={Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society},
volume={8},
number={2},
pages={1758--1770},
year={2025}
}
High-dimensional causal mediation analysis by partial sum statistic and sample splitting strategy in imaging genetics application
@article{chang2025high,
title={High-dimensional causal mediation analysis by partial sum statistic and sample splitting strategy in imaging genetics application},
author={Chang, Hung-Ching and Fang, Yusi and Gorczyca, Michael T and Batmanghelich, Kayhan and Tseng, George C},
journal={Bioinformatics},
volume={41},
number={10},
pages={btaf493},
year={2025},
publisher={Oxford University Press}
}
Performance of Natural Language Processing versus International Classification of Diseases Codes in Building Registries for Patients With Fall Injury: Retrospective Analysis
@article{taseh2025performance,
title={Performance of Natural Language Processing versus International Classification of Diseases Codes in Building Registries for Patients With Fall Injury: Retrospective Analysis},
author={Taseh, Atta and Sasanfar, Souri and Chan, Michelle and Sirls, Evan and Nazarian, Ara and Batmanghelich, Kayhan and Bean, Jonathan F and Ashkani-Esfahani, Soheil},
journal={JMIR Medical Informatics},
volume={13},
number={1},
pages={e66973},
year={2025},
publisher={JMIR Publications Inc., Toronto, Canada}
}
Multi-Modal Large Language Models are Effective Vision Learners
@inproceedings{sun2025multi,
title={Multi-Modal Large Language Models are Effective Vision Learners},
author={Sun, Li and Ahuja, Chaitanya and Chen, Peng and D'Zmura, Matt and Batmanghelich, Kayhan and Bontrager, Philip},
booktitle={2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
pages={8617--8626},
year={2025},
organization={IEEE}
}
2024
MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images
@article{xu2024medsyn,
title={MedSyn: text-guided anatomy-aware synthesis of high-fidelity 3-D CT images},
author={Xu, Yanwu and Sun, Li and Peng, Wei and Jia, Shuyue and Morrison, Katelyn and Perer, Adam and Zandifar, Afrooz and Visweswaran, Shyam and Eslami, Motahhare and Batmanghelich, Kayhan},
journal={IEEE Transactions on Medical Imaging},
volume={43},
number={10},
pages={3648--3660},
year={2024},
publisher={IEEE}
}
Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography
@inproceedings{ghosh2024mammo,
title={Mammo-clip: A vision language foundation model to enhance data efficiency and robustness in mammography},
author={Ghosh, Shantanu and Poynton, Clare B and Visweswaran, Shyam and Batmanghelich, Kayhan},
booktitle={International conference on medical image computing and computer-assisted intervention},
pages={632--642},
year={2024},
organization={Springer Nature Switzerland Cham}
}
Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning
@article{yu2024anatomy,
title={Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning},
author={Yu, Ke and Ghosh, Shantanu and Liu, Zhexiong and Deible, Christopher and Poynton, Clare B and Batmanghelich, Kayhan},
journal={Radiology: Artificial Intelligence},
volume={6},
number={5},
pages={e230277},
year={2024},
publisher={Radiological Society of North America}
}
2023
Semi-Implicit Denoising Diffusion Models (SIDDMs)
@inproceedings{gong2023semi,
title={Semi-implicit denoising diffusion models (siddms)},
author={Gong, Mingming and Xie, Shaoan and Wei, Wei and Grundmann, Matthias and Batmanghelich, Kayhan and Hou, Tingbo and others},
booktitle={Advances in Neural Information Processing Systems},
volume={36},
pages={17383--17394},
year={2023}
}
DrasCLR: Self-Supervised Representation Learning via Disentangled Representations and Spectral Clustering
@inproceedings{sun2021context,
title={Context matters: Graph-based self-supervised representation learning for medical images},
author={Sun, Li and Yu, Ke and Batmanghelich, Kayhan},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={35},
number={6},
pages={4874--4882},
year={2021}
}
Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics
@article{murali2023beyond,
title={Beyond distribution shift: Spurious features through the lens of training dynamics},
author={Murali, Nihal and Puli, Aahlad and Yu, Ke and Ranganath, Rajesh and Batmanghelich, Kayhan},
journal={Transactions on machine learning research},
volume={2023},
pages={https--openreview},
year={2023}
}
ComBat Harmonization: Empirical Bayes versus fully Bayes approaches
@article{reynolds2023combat,
title={Combat harmonization: Empirical bayes versus fully bayes approaches},
author={Reynolds, Maxwell and Chaudhary, Tigmanshu and Torbati, Mahbaneh Eshaghzadeh and Tudorascu, Dana L and Batmanghelich, Kayhan and Alzheimer's Disease Neuroimaging Initiative and others},
journal={NeuroImage: Clinical},
volume={39},
pages={103472},
year={2023},
publisher={Elsevier}
}
Distilling Blackbox to Interpretable Models for Efficient Transfer Learning
@inproceedings{ghosh2023distilling,
title={Distilling blackbox to interpretable models for efficient transfer learning},
author={Ghosh, Shantanu and Yu, Ke and Batmanghelich, Kayhan},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={628--638},
year={2023},
organization={Springer Nature Switzerland Cham}
}
Physics-Informed Neural Networks for Tissue Elasticity Reconstruction in Magnetic Resonance Elastography
@inproceedings{ragoza2023physics,
title={Physics-informed neural networks for tissue elasticity reconstruction in magnetic resonance elastography},
author={Ragoza, Matthew and Batmanghelich, Kayhan},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={333--343},
year={2023},
organization={Springer Nature Switzerland Cham}
}
Deep Learning Integration of Chest CT Imaging and Gene Expression Identifies Novel Aspects of COPD
@article{chen2023deep,
title={Deep learning integration of chest computed tomography imaging and gene expression identifies novel aspects of copd},
author={Chen, Junxiang and Xu, Zhonghui and Sun, Li and Yu, Ke and Hersh, Craig P and Boueiz, Adel and Hokanson, John E and Sciurba, Frank C and Silverman, Edwin K and Castaldi, Peter J and others},
journal={Chronic Obstructive Pulmonary Diseases: Journal of the COPD Foundation},
volume={10},
number={4},
pages={355},
year={2023}
}
Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat
@inproceedings{ghosh2023distilling,
title={Distilling blackbox to interpretable models for efficient transfer learning},
author={Ghosh, Shantanu and Yu, Ke and Batmanghelich, Kayhan},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={628--638},
year={2023},
organization={Springer Nature Switzerland Cham}
}
Augmentation by Counterfactual Explanation — Fixing an Overconfident Classifier
@inproceedings{singla2023augmentation,
title={Augmentation by counterfactual explanation-fixing an overconfident classifier},
author={Singla, Sumedha and Murali, Nihal and Arabshahi, Forough and Triantafyllou, Sofia and Batmanghelich, Kayhan},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={4720--4730},
year={2023}
}
Explaining the Black-box Smoothly – A Counterfactual Approach
@article{singla2023explaining,
title={Explaining the black-box smoothly—a counterfactual approach},
author={Singla, Sumedha and Eslami, Motahhare and Pollack, Brian and Wallace, Stephen and Batmanghelich, Kayhan},
journal={Medical Image Analysis},
volume={84},
pages={102721},
year={2023},
publisher={Elsevier}
}
2022
Automated Detection of Premalignant Oral Lesions on Whole Slide Images Using CNN
@article{liu2022automated,
title={Automated detection of premalignant oral lesions on whole slide images using convolutional neural networks},
author={Liu, Yingci and Bilodeau, Elizabeth and Pollack, Brian and Batmanghelich, Kayhan},
journal={Oral Oncology},
volume={134},
pages={106109},
year={2022},
publisher={Pergamon}
}
Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays
@inproceedings{yu2022anatomy,
title={Anatomy-guided weakly-supervised abnormality localization in chest x-rays},
author={Yu, Ke and Ghosh, Shantanu and Liu, Zhexiong and Deible, Christopher and Batmanghelich, Kayhan},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={658--668},
year={2022},
organization={Springer Nature Switzerland Cham}
}
Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation
@inproceedings{xu2022adversarial,
title={Adversarial consistency for single domain generalization in medical image segmentation},
author={Xu, Yanwu and Xie, Shaoan and Reynolds, Maxwell and Ragoza, Matthew and Gong, Mingming and Batmanghelich, Kayhan},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={671--681},
year={2022},
organization={Springer Nature Switzerland Cham}
}
Hierarchical Amortized Training for Memory-efficient High-Resolution 3D GAN
@article{sun2022hierarchical,
title={Hierarchical amortized GAN for 3D high resolution medical image synthesis},
author={Sun, Li and Chen, Junxiang and Xu, Yanwu and Gong, Mingming and Yu, Ke and Batmanghelich, Kayhan},
journal={IEEE journal of biomedical and health informatics},
volume={26},
number={8},
pages={3966--3975},
year={2022},
publisher={IEEE}
}
Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation
@inproceedings{xu2022maximum,
title={Maximum spatial perturbation consistency for unpaired image-to-image translation},
author={Xu, Yanwu and Xie, Shaoan and Wu, Wenhao and Zhang, Kun and Gong, Mingming and Batmanghelich, Kayhan},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={18311--18320},
year={2022}
}
Knowledge Distillation via Constrained Variational Inference
@inproceedings{saeedi2022knowledge,
title={Knowledge distillation via constrained variational inference},
author={Saeedi, Ardavan and Utsumi, Yuria and Sun, Li and Batmanghelich, Kayhan and Lehman, Li-wei},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={36},
number={7},
pages={8132--8140},
year={2022}
}
2021
Can Contrastive Learning Avoid Shortcut Solutions?
@inproceedings{robinson2021can,
title={Can contrastive learning avoid shortcut solutions?},
author={Robinson, Joshua and Sun, Li and Yu, Ke and Batmanghelich, Kayhan and Jegelka, Stefanie and Sra, Suvrit},
journal={Advances in neural information processing systems},
volume={34},
pages={4974--4986},
year={2021}
}
Deep Learning Prediction of Voxel-Level Liver Stiffness in Patients with Nonalcoholic Fatty Liver Disease
@article{pollack2021deep,
title={Deep learning prediction of voxel-level liver stiffness in patients with nonalcoholic fatty liver disease},
author={Pollack, Brian L and Batmanghelich, Kayhan and Cai, Stephen S and Gordon, Emile and Wallace, Stephen and Catania, Roberta and Morillo-Hernandez, Carlos and Furlan, Alessandro and Borhani, Amir A},
journal={Radiology: Artificial Intelligence},
volume={3},
number={6},
pages={e200274},
year={2021},
publisher={Radiological Society of North America}
}
Self-Supervised Vessel Enhancement Using Flow-Based Consistencies
@inproceedings{jena2021self,
title={Self-supervised vessel enhancement using flow-based consistencies},
author={Jena, Rohit and Singla, Sumedha and Batmanghelich, Kayhan},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={242--251},
year={2021},
organization={Springer International Publishing Cham}
}
Using Causal Analysis for Conceptual Deep Learning Explanation
@inproceedings{singla2021using,
title={Using causal analysis for conceptual deep learning explanation},
author={Singla, Sumedha and Wallace, Stephen and Triantafillou, Sofia and Batmanghelich, Kayhan},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={519--528},
year={2021},
organization={Springer International Publishing Cham}
}
Empowering Variational Inference with Predictive Features: Application to Disease Subtyping
Improving Clinical Disease Sub-typing and Future Events Prediction through a Chest CT based Deep Learning Approach
@article{singla2021improving,
title={Improving clinical disease subtyping and future events prediction through a chest CT-based deep learning approach},
author={Singla, Sumedha and Gong, Mingming and Riley, Craig and Sciurba, Frank and Batmanghelich, Kayhan},
journal={Medical physics},
volume={48},
number={3},
pages={1168--1181},
year={2021}
}
Context Matters: Graph-based Self-supervised Representation Learning for Medical Images
@inproceedings{sun2021context,
title={Context matters: Graph-based self-supervised representation learning for medical images},
author={Sun, Li and Yu, Ke and Batmanghelich, Kayhan},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={35},
number={6},
pages={4874--4882},
year={2021}
}
2020
Unpaired Data Empowers Association Tests
@article{gong2019unpaired,
title={Unpaired Data Empowers Association Tests},
author={Gong, Mingming and Liu, Peng and Sciurba, Frank C and Stojanov, Petar and Tao, Dacheng and Tseng, George and Zhang, Kun and Batmanghelich, Kayhan},
journal={Bioinformatics},
pages={839159},
year={2019},
publisher={Cold Spring Harbor Laboratory}
}
Label-Noise Robust Domain Adaptation
@inproceedings{yu2020label,
title={Label-noise robust domain adaptation},
author={Yu, Xiyu and Liu, Tongliang and Gong, Mingming and Zhang, Kun and Batmanghelich, Kayhan and Tao, Dacheng},
booktitle={International conference on machine learning},
pages={10913--10924},
year={2020},
organization={PMLR}
}
Semi-Supervised Hierarchical Drug Embedding
@article{yu2020semi,
title={Semi-supervised hierarchical drug embedding in hyperbolic space},
author={Yu, Ke and Visweswaran, Shyam and Batmanghelich, Kayhan},
journal={Journal of chemical information and modeling},
volume={60},
number={12},
pages={5647--5657},
year={2020},
publisher={ACS Publications}
}
3D-BoxSup: Positive-Unlabeled Learning of Brain Tumor Segmentation Networks From 3D Bounding Boxes
@article{xu20203d,
title={3d-boxsup: Positive-unlabeled learning of brain tumor segmentation networks from 3d bounding boxes},
author={Xu, Yanwu and Gong, Mingming and Chen, Junxiang and Chen, Ziye and Batmanghelich, Kayhan},
journal={Frontiers in Neuroscience},
volume={14},
pages={350},
year={2020},
publisher={Frontiers Media SA}
}
Human-Machine Collaboration for Medical Image Segmentation
@inproceedings{ravanbakhsh2020human,
title={Human-machine collaboration for medical image segmentation},
author={Ravanbakhsh, Mahdyar and Tschernezki, Vadim and Last, Felix and Klein, Tassilo and Batmanghelich, Kayhan and Tresp, Volker and Nabi, Moin},
booktitle={ICASSP 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={1040--1044},
year={2020},
organization={IEEE}
}
Explanation by Progressive Exaggeration
@inproceedings{singla2019explanation,
title={Explanation by Progressive Exaggeration},
author={Singla, Sumedha and Pollack, Brian and Chen, Junxiang and Batmanghelich, Kayhan},
booktitle={International Conference on Learning Representations},
year={2019}
}
Generative-Discriminative Complementary Learning
@inproceedings{xu2020generative,
title={Generative-discriminative complementary learning},
author={Xu, Yanwu and Gong, Mingming and Chen, Junxiang and Liu, Tongliang and Zhang, Kun and Batmanghelich, Kayhan},
booktitle={Proceedings of the AAAI conference on artificial intelligence},
volume={34},
number={04},
pages={6526--6533},
year={2020}
}
Weakly Supervised Disentanglement by Pairwise Similarities
@inproceedings{chen2020weakly,
title={Weakly supervised disentanglement by pairwise similarities},
author={Chen, Junxiang and Batmanghelich, Kayhan},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={34},
number={04},
pages={3495--3502},
year={2020}
}
2019
Geometry-Consistent Adversarial Networks for One-Sided Unsupervised Domain Mapping (GcGAN)
@inproceedings{fu2019geometry,
title={Geometry-consistent generative adversarial networks for one-sided unsupervised domain mapping},
author={Fu, Huan and Gong, Mingming and Wang, Chaohui and Batmanghelich, Kayhan and Zhang, Kun and Tao, Dacheng},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={2427--2436},
year={2019}
}
Twin Auxiliary Classifiers GAN
@inproceedings{gong2019twin,
title={Twin auxilary classifiers gan},
author={Gong, Mingming and Xu, Yanwu and Li, Chunyuan and Zhang, Kun and Batmanghelich, Kayhan},
booktitle={Advances in neural information processing systems},
volume={32},
year={2019}
}
Generative Interpretability: Application in Disease Subtyping
Robust Ordinal VAE: Employing Noisy Pairwise Comparisons for Disentanglement
@online{chen2019robust,
author = {Chen, Junxiang and Batmanghelich, Kayhan},
title = {Robust ordinal VAE: employing noisy pairwise comparisons for disentanglement},
year = {2019},
eprint = {1910.05898},
eprinttype = {arXiv},
eprintclass = {cs.LG},
url = {https://arxiv.org/abs/1910.05898},
note = {Preprint},
keywords = {preprint}
}
2018
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector
@inproceedings{singla2018subject2vec,
title={Subject2Vec: generative-discriminative approach from a set of image patches to a vector},
author={Singla, Sumedha and Gong, Mingming and Ravanbakhsh, Siamak and Sciurba, Frank and Poczos, Barnabas and Batmanghelich, Kayhan N},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={502--510},
year={2018},
organization={Springer International Publishing Cham}
}
A structural equation model for imaging genetics using spatial transcriptomics
@article{huisman2018structural,
title={A structural equation model for imaging genetics using spatial transcriptomics},
author={Huisman, Sjoerd MH and Mahfouz, Ahmed and Batmanghelich, Nematollah K and Lelieveldt, Boudewijn PF and Reinders, Marcel JT and Alzheimer's Disease Neuroimaging Initiative},
journal={Brain informatics},
volume={5},
number={2},
pages={13},
year={2018},
publisher={Springer Berlin Heidelberg Berlin/Heidelberg}
}
Causal Generative Domain Adaptation Networks
@online{gong2018causal,
author = {Gong, Mingming and Zhang, Kun and Huang, Biwei and Glymour, Clark and Tao, Dacheng and Batmanghelich, Kayhan},
title = {Causal generative domain adaptation networks},
year = {2018},
eprint = {1804.04333},
eprinttype = {arXiv},
eprintclass = {cs.LG},
url = {https://arxiv.org/abs/1804.04333},
note = {Preprint},
keywords = {preprint}
}
Deep Diffeomorphic Normalizing Flows
@online{salman2018deep,
author = {Salman, Hadi and Yadollahpour, Payman and Fletcher, Tom and Batmanghelich, Kayhan},
title = {Deep diffeomorphic normalizing flows},
year = {2018},
eprint = {1810.03256},
eprinttype = {arXiv},
eprintclass = {cs.LG},
url = {https://arxiv.org/abs/1810.03256},
note = {Preprint},
keywords = {preprint}
}
An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption
@inproceedings{yu2018efficient,
title={An efficient and provable approach for mixture proportion estimation using linear independence assumption},
author={Yu, Xiyu and Liu, Tongliang and Gong, Mingming and Batmanghelich, Kayhan and Tao, Dacheng},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={4480--4489},
year={2018}
}
Deep Ordinal Regression Network for Monocular Depth Estimation
@inproceedings{fu2018deep,
title={Deep ordinal regression network for monocular depth estimation},
author={Fu, Huan and Gong, Mingming and Wang, Chaohui and Batmanghelich, Kayhan and Tao, Dacheng},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={2002--2011},
year={2018}
}
Textured Graph-Based Model of the Lungs: Application on Tuberculosis Type Classification and Multi-drug Resistance Detection
2017
Transformations Based on Continuous Piecewise-Affine Velocity Fields
@article{freifeld2017transformations,
title={Transformations based on continuous piecewise-affine velocity fields},
author={Freifeld, Oren and Hauberg, S{\o}ren and Batmanghelich, Kayhan and Fisher, Jonn W},
journal={IEEE transactions on pattern analysis and machine intelligence},
volume={39},
number={12},
pages={2496--2509},
year={2017},
publisher={IEEE}
}
A Likelihood-Free Approach for Characterizing Heterogeneous Diseases in Large-Scale Studies
@inproceedings{schabdach2017likelihood,
title={A likelihood-free approach for characterizing heterogeneous diseases in large-scale studies},
author={Schabdach, Jenna and Wells III, William M and Cho, Michael and Batmanghelich, Kayhan N},
booktitle={International Conference on Information Processing in Medical Imaging},
pages={170--183},
year={2017},
organization={Springer International Publishing Cham}
}
2016
Unsupervised Discovery of Emphysema Subtypes in a Large Clinical Cohort
@inproceedings{binder2016unsupervised,
title={Unsupervised discovery of emphysema subtypes in a large clinical cohort},
author={Binder, Polina and Batmanghelich, Nematollah K and Estepar, Raul San Jose and Golland, Polina},
booktitle={International Workshop on Machine Learning in Medical Imaging},
pages={180--187},
year={2016},
keywords={workshop},
organization={Springer International Publishing Cham}
}
Probabilistic Modeling of Imaging, Genetics and the Diagnosis
@article{batmanghelich2016probabilistic,
title={Probabilistic modeling of imaging, genetics and diagnosis},
author={Batmanghelich, Nematollah K and Dalca, Adrian and Quon, Gerald and Sabuncu, Mert and Golland, Polina},
journal={IEEE transactions on medical imaging},
volume={35},
number={7},
pages={1765--1779},
year={2016},
publisher={IEEE}
}
Nonparametric Spherical Topic Modeling with Word Embeddings
@inproceedings{batmanghelich-etal-2016-nonparametric,
title = "Nonparametric Spherical Topic Modeling with Word Embeddings",
author = "Batmanghelich, Kayhan and
Saeedi, Ardavan and
Narasimhan, Karthik and
Gershman, Sam",
editor = "Erk, Katrin and
Smith, Noah A.",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P16-2087/",
doi = "10.18653/v1/P16-2087",
pages = "537--542"
}
Inferring Disease Status by non-Parametric Probabilistic Embedding
@inproceedings{batmanghelich2016inferring,
title={Inferring Disease Status by Non-parametric Probabilistic Embedding},
author={Batmanghelich, Nematollah Kayhan and Saeedi, Ardavan and Estepar, Raul San Jose and Cho, Michael and Wells III, William M},
booktitle={Bayesian and grAphical Models for Biomedical Imaging},
pages={49--57},
year={2016},
keywords={workshop},
publisher={Springer International Publishing Cham}
}
2015
Highly-Expressive Spaces of Well-Behaved Transformations: Keeping It Simple
@inproceedings{freifeld2015highly,
title={Highly-expressive spaces of well-behaved transformations: Keeping it simple},
author={Freifeld, Oren and Hauberg, Soren and Batmanghelich, Kayhan and Fisher, John W},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={2911--2919},
year={2015}
}
Generative Method to Discover Genetically Driven Image Biomarkers
@inproceedings{batmanghelich2015generative,
title={Generative method to discover genetically driven image biomarkers},
author={Batmanghelich, Nematollah K and Saeedi, Ardavan and Cho, Michael and Estepar, Raul San Jose and Golland, Polina},
booktitle={International Conference on Information Processing in Medical Imaging},
pages={30--42},
year={2015},
organization={Springer International Publishing Cham}
}
2014
Spherical Topic Models for Imaging Phenotype Discovery in Genetic Studies
@inproceedings{batmanghelich2014spherical,
title={Spherical topic models for imaging phenotype discovery in genetic studies},
author={Batmanghelich, Kayhan N and Cho, Michael and Jose, Raul San and Golland, Polina},
booktitle={Bayesian and grAphical Models for Biomedical Imaging: First International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers},
pages={107--117},
year={2014},
keywords={workshop},
organization={Springer International Publishing Cham}
}
Diversifying Sparsity Using Variational Determinantal Point Processes
@online{batmanghelich2014diversifying,
author = {Batmanghelich, Nematollah Kayhan and Quon, Gerald and Kulesza, Alex and Kellis, Manolis and Golland, Polina and Bornn, Luke},
title = {Diversifying sparsity using variational determinantal point processes},
year = {2014},
eprint = {1411.6307},
eprinttype = {arXiv},
eprintclass = {stat.ML},
url = {https://arxiv.org/abs/1411.6307},
note = {Preprint},
keywords = {preprint}
}
BrainPrint in the Computer-Aided Diagnosis of Alzheimer’s Disease
@inproceedings{wachinger2014brainprint,
title={BrainPrint in the computer-aided diagnosis of Alzheimer's disease},
author={Wachinger, Christian and Batmanghelich, K and Golland, Polina and Reuter, Martin},
booktitle={Proceedings MICCAI workshop challenge on computer-aided diagnosis of dementia based on structural MRI data, Boston, MA, USA},
keywords={workshop},
year={2014}
}
2013
Joint Modeling of Imaging and Genetics
@inproceedings{batmanghelich2013joint,
title={Joint modeling of imaging and genetics},
author={Batmanghelich, Nematollah K and Dalca, Adrian V and Sabuncu, Mert R and Golland, Polina},
booktitle={International Conference on Information Processing in Medical Imaging},
pages={766--777},
year={2013},
organization={Springer Berlin Heidelberg Berlin, Heidelberg}
}
2012
Dominant Component Analysis of Electro-Physiological Connectivity Network
@inproceedings{ghanbari2012dominant,
title={Dominant component analysis of electrophysiological connectivity networks},
author={Ghanbari, Yasser and Bloy, Luke and Batmanghelich, Kayhan and Roberts, Timothy PL and Verma, Ragini},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={231--238},
year={2012},
organization={Springer Berlin Heidelberg Berlin, Heidelberg}
}
An integrated Framework for High Angular Resolution Diffusion Imaging-Based Investigation of Structural Connectivity
@article{bloy2012integrated,
title={An Integrated Framework for High Angular Resolution Diffusion Imaging--Based Investigation of Structural Connectivity},
author={Bloy, Luke and Ingalhalikar, Madhura and Batmanghelich, Nematollah K and Schultz, Robert T and Roberts, Timothy PL and Verma, Ragini},
journal={Brain connectivity},
volume={2},
number={2},
pages={69--79},
year={2012},
publisher={Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA}
}
Generative-Discriminative Basis Learning for Medical Imaging
@article{batmanghelich2011generative,
title={Generative-discriminative basis learning for medical imaging},
author={Batmanghelich, Nematollah K and Taskar, Ben and Davatzikos, Christos},
journal={IEEE transactions on medical imaging},
volume={31},
number={1},
pages={51--69},
year={2011},
publisher={IEEE}
}
2011
Regularized Tensor Factorization for Multi-Modality Medical Image Classification
@inproceedings{batmanghelich2011regularized,
title={Regularized tensor factorization for multi-modality medical image classification},
author={Batmanghelich, Nematollah and Dong, Aoyan and Taskar, Ben and Davatzikos, Christos},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={17--24},
year={2011},
organization={Springer Berlin Heidelberg Berlin, Heidelberg}
}
Disease Classification and Prediction via Semi-Supervised Dimensionality Reduction
@inproceedings{batmanghelich2011disease,
title={Disease classification and prediction via semi-supervised dimensionality reduction},
author={Batmanghelich, Kayhan N and Dong, H Ye and Pohl, Kilian M and Taskar, Ben and Davatzikos, Christos and others},
booktitle={2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro},
pages={1086--1090},
year={2011},
organization={IEEE}
}
2010
Prediction of MCI Conversion via MRI, CSF Biomarkers, and Pattern Classification
@article{davatzikos2011prediction,
title={Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification},
author={Davatzikos, Christos and Bhatt, Priyanka and Shaw, Leslie M and Batmanghelich, Kayhan N and Trojanowski, John Q},
journal={Neurobiology of aging},
volume={32},
number={12},
pages={2322--e19},
year={2011},
publisher={Elsevier}
}
Application of Trace-Norm and Low-Rank Matrix Decomposition for Computational Anatomy
@inproceedings{batmanghelich2010application,
title={Application of trace-norm and low-rank matrix decomposition for computational anatomy},
author={Batmanghelich, Nematollah and Gooya, Ali and Kanterakis, Stathis and Taskar, Ben and Davatzikos, Christos},
booktitle={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops},
pages={146--153},
year={2010},
keywords={workshop},
organization={IEEE}
}
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