Publications

The full list of my articles can also be found on my Google Scholar profile.

Published articles are linked to in the titles when available. Public author versions are linked to in the titles (if in press), AAM (author accepted manuscript) or Preprint.

Deep Learning and fMRI

• Wang, S., and Dvornek, N.C.
  A Metamodel Structure For Regression Analysis: Application To Prediction Of Autism Spectrum Disorder Severity
  To appear in IEEE International Symposium on Biomedical Imaging (ISBI), 2021.

• Schirmer, M.D., Venkataraman, A., Rekik, I., Kim, M., Mostofsky, S.H., Nebel, M.B., Rosch, K., Seymour, K., Crocetti, D., Irzan, H., Hütel, M., Ourselin,S ., Marlow, N., Melbourne, A., Levchenko, D., Zhou, S., Kunda, M., Lu, H., Dvornek, N.C, Zhuang, J., Pinto, G., Samal, S., Zhang, J., Bernal-Rusiel, J.L., Pienaar, R., and Chung, A. W.
  Neuropsychiatric Disease Classification Using Functional Connectomics-Results of the Connectomics in NeuroImaging Transfer Learning Challenge
  Medical Image Analysis, 2021, pp. 101972

• Dvornek, N.C., Li, X., Zhuang, J., and Duncan, J.S.
  Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity
  Machine Learning in Medical Imaging (MLMI, a MICCAI Workshop), 2020, LNCS 12436, pp. 363-372.
  [AAM]

• Li, X., Zhou, Y., Dvornek, N.C., Zhang, M., Zhuang, J., Ventola, P. and Duncan, J.S.
  Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2020, LNCS 12267, pp. 625-635.
  [Slides] [AAM]

• Li, X., Gu, Y., Dvornek, N., Staib, L.H., Ventola, P. and Duncan, J.S.
  Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results
  Medical Image Analysis, vol. 65, 2020, pp. 101765.
  [AAM]

• Dvornek, N.C., Ventola, P., Duncan, J.S.
  Estimating Reproducible Functional Networks Associated with Task Dynamics Using Unsupervised LSTMS
  IEEE International Symposium on Biomedical Imaging (ISBI), 2020.
  [Slides] [AAM]

• Dvornek, N.C., Li, X., Zhuang, J., and Duncan, J.S.
  Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI
  Machine Learning in Medical Imaging (MLMI, a MICCAI Workshop), 2019, LNCS 11861, pp. 382-390.
  [Slides] [AAM]
  *Best Paper Award* (1/158 submissions)

• Zhuang, J., Dvornek, N.C., Li, X., Ventola, P. and Duncan, J.S.
  Invertible Network for Classification and Biomarker Selection for ASD
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019, LNCS 11766, pp. 700–708.
  [AAM]

• Li, X., Dvornek, N.C., Zhou, Y., Zhuang, J., Ventola, P. and Duncan, J.S.
  Graph Neural Network for Interpreting Task-fMRI Biomarkers
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019, LNCS 11768, pp. 485-493.
  [AAM]

• Li, X., Dvornek, N.C., Zhou, Y., Zhuang, J., Ventola, P. and Duncan, J.S.
  Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery
  Information Processing in Medical Imaging (IPMI), 2019, LNCS 11492, pp. 718-730.
  [AAM]

• Dvornek, N.C., Yang, D., Ventola, P., Duncan J.S.
  Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018, LNCS 11072, pp. 329–337.
  [Poster] [AAM]

• Li, X., Dvornek, N.C., Zhuang, J., Ventola, P. and Duncan, J.S.
  Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI
  Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2018, LNCS 11072, pp. 206-214.
  [AAM]

• Li, X., Dvornek, N., Papademetris, X., Zhuang, J., Staib, L.H., Ventola, P., Duncan, J.
  2-Channel Convolutional 3D Deep Neural Network (2CC3D) for fMRI Analysis: ASD Classification and Feature Learning
  IEEE International Symposium on Biomedical Imaging (ISBI), April 2018, pp. 1252-1255.

• Dvornek, N.C., Ventola, P., Duncan, J.S.
  Combining Phenotypic and Resting-State fMRI Data for Autism Classification with Recurrent Neural Networks
  IEEE International Symposium on Biomedical Imaging (ISBI), April 2018, pp. 725-528.
  [Poster] [AAM]

• Dvornek, N.C., Ventola, P., Pelphrey, K.A., Duncan, J.S.
  Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks
  International Workshop on Machine Learning in Medical Imaging, 2017, LNCS 10541, pp. 362-370.
  [Slides] [AAM]

Deep Learning in Segmentation

• Yang, J., Li, X., Pak, D., Dvornek, N.C., Chapiro, J., Lin, M.D., Duncan, J.S.
  Cross-Modality Segmentation by Self-supervised Semantic Alignment in Disentangled Content Space
  Domain Adaptation and Representation Transfer (DART, a MICCAI Workshop), 2020, LNCS 12444, pp. 52-61.
  *Best Paper Award*

• Yang, J., Dvornek, N.C., Zhang, F., Zhuang, J., Chapiro, J., Lin, M. and Duncan, J.S.
  Domain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation
  Visual Recognition for Medical Images (VRMI, IEEE/CVF International Conference on Computer Vision Workshop), 2019.
  [AAM]

• Zhuang, J., Yang, J., Gu, L. and Dvornek, N.C.,
  ShelfNet for Fast Semantic Segmentation
  Computer Vision for Road Scene Understanding and Autonomous Driving, (CVRSUAD, IEEE/CVF International Conference on Computer Vision Workshop), 2019.
  [Code] [AAM]

• Yang, J., Dvornek, N.C., Zhang, F., Chapiro, J., Lin, M. and Duncan, J.S.
  Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019, LNCS 11765, pp. 255-263.
  [AAM]

Other Deep Learning

• Zhuang, J., Dvornek, N.C., Tatikonda, S.C., Duncan, J.
  MALI: A memory efficient and reverse accurate integrator for Neural ODEs
  To appear in International Conference on Learning Representations (ICLR) 2021

• Zhuang, J., Tang, T. Tatikonda, S.C., Dvornek, N., Ding, Y., Papademetris, X., Duncan, J.
  AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
  To appear in Neural Information Processing Systems (NeurIPS) 2020
  [Code] [Webpage]
  Spotlight Presentation (<3% of submissions)

• Zhang, F., Dvornek, N., Yang, J., Chapiro., J., Duncan, J.
  Layer Embedding Analysis in Convolutional Neural Networks for Improved Probability Calibration and Classification
  IEEE Transactions on Medical Imaging, In Press., 2020.

• Li, X., Zhou, Y., Dvornek, N.C., Gu, Y., Ventola, P. and Duncan, J.S.
  Efficient Shapley Explanation for Features Importance Estimation Under Uncertainty
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2020, LNCS 12261, pp. 792-801.

• Zhuang, J., Dvornek, N., Li, X., Tatikonda, S., Papademetris, X., Duncan, J.S.
  Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
  International Conference on Machine Learning (ICML), 2020, PMLR 119.
  [AAM]

Other Machine Learning

• Zhuang, J., Dvornek, N.C., Zhao, Q., Li, X., Ventola, P. and Duncan, J.S.
  Prediction of treatment outcome for autism from structure of the brain based on sure independence screening
  IEEE International Symposium on Biomedical Imaging (ISBI), April 2019.
  [AAM]

• Zhuang, J., Dvornek, N.C., Li, X., Ventola, P. and Duncan, J.S.
  Prediction of Severity and Treatment Outcome for ASD from fMRI
  International Workshop on PRedictive Intelligence In MEdicine, 2018, LNCS 11121, pp. 9-17.
  [AAM]

• Zhuang, J., Dvornek, N., Li, X., Yang, D., Ventola, P., Duncan, J.
  Prediction of pivotal response treatment outcome with task fMRI using random forest and variable selection
  IEEE International Symposium on Biomedical Imaging (ISBI), April 2018, pp. 97-100.

• Yang, D., Pelphrey, K.A., Sukholdolsky, D., Crowley, M., Dayan, E., Dvornek, N.C., Venkataraman, A., Duncan, J.S., Staib, L.H., Ventola, P.
  Brain Responses to Biological Motion Predict Treatment Outcome in Young Children with Autism
  Translational Psychiatry, 2016, vol. 6, no. 11, e948.

• Dvornek, N.C., Yang, D., Venkataraman, A., Ventola, P., Staib, L.H., Pelphrey, K.A., Duncan, J.S.
  Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging
  International Workshop on Multimodal Learning for Clinical Decision Support, 2016.
  [Slides]

• Venkataraman, A, Yang, D.Y.J., Dvornek, N, Staib, L.H., Duncan, J.S., Pelphrey, K.A., Ventola, P.
  Pivotal response treatment prompts a functional rewiring of the brain among individuals with autism spectrum disorder
  Neuroreport, 2016, vol. 27, no. 14, pp. 1081-1085.
  [AAM]

• Dvornek, N.C., Sigworth, F.J., and Tagare, H.D.
  SubspaceEM: A Fast Maximum-a-posteriori Algorithm for Cryo-EM Single Particle Reconstruction
  Journal of Structural Biology, 2015, vol. 190, no. 2, pp. 200-214.
  [Code] [AAM]

Image Registration

• Chitphakdithai, N.
  Registration of Pre- and Post-Treatment Brain Images with Missing Correspondences
  Ph.D. Dissertation, Yale University, 2012.

• Chitphakdithai, N., Chiang, V.L., and Duncan, J.S.
  Tracking Metastatic Brain Tumors in Longitudinal Scans via Joint Image Registration and Labeling
  Spatiotemporal Image Analysis for Longitudinal and Time-Series Image Data, 2012, LNCS 7570, pp. 124-136.
  [AAM]

• Chitphakdithai, N., Chiang, V.L., and Duncan, J.S.
  Non-rigid Registration of Longitudinal Brain Tumor Treatment MRI
  International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 4893-4896.
  [Slides] [AAM]

• Chitphakdithai, N., Vives, K.P., and Duncan, J.S.
  Registration of Brain Resection MRI with Intensity and Location Priors
  IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011, pp. 1520-1523.
  [Slides] [AAM]

• Chitphakdithai, N. and Duncan, J.S.
  Non-rigid Registration with Missing Correspondences in Preoperative and Postresection Brain Images
  Medical Image Computing and Computer Assisted Intervention (MICCAI), 2010, Part I, LNCS 6361, pp. 367-374.
  [Slides] [AAM]

• Chitphakdithai, N. and Duncan, J.S.
  Pairwise Registration of Images With Missing Correspondences Due to Resection
  IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), 2010, pp. 1025-1028.
  [Slides] [AAM]

Conference Proceedings following Peer-Reviewed Abstract

• Li, X., Dvornek, N.C., Zhuang, J., Ventola, P. and Duncan, J.
  Graph embedding using infomax for ASD classification and brain functional difference detection
  Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging (SPIE Medical Imaging), 2020, Vol. 11317, p. 1131702.
  [AAM]

• Guo, Y., Dvornek, N., Lu, Y., Tsai, Y.J., Hamill, J., Casey, M. and Liu, C.
  Deep Learning based Respiratory Pattern Classification and Applications in PET/CT Motion Correction
  IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2019.
  [Preprint]