Software

Code Supporting Publications

• Singla A et al.: Multiple Instance Neuroimage Transformer, Predictive Intelligence in Medicine, Springer, Lecture Notes in Computer Science, 2022

• Vento A et al.: A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Models, Medical Image Computing and Computer Assisted Intervention, Springer-Verlag, 2022

• Endo M et al.: GaitForeMer: Self-Supervised Pre-Training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation, Medical Image Computing and Computer Assisted Intervention, Springer-Verlag, 2022

• Li Y et al.: Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome, Medical Image Computing and Computer-Assisted Intervention, Springer-Verlag, 2022

• Paschali M et al.: Bridging the Gap Between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing, Predictive Intelligence in Medicine, Springer, Lecture Notes in Computer Science, 2022

• Ouyang et al.: Disentangling Normal Aging from Severity of Disease via Weak Supervision on Longitudinal MRI, IEEE Transactions on Medical Imaging, 2022.

• Butskova A et al.: Adversarial Bayesian Optimization for Quantifying Motion Artifact within MRI, PRedictive Intelligence in MEdicine, 2021

• Zhao Q et al.: Longitudinal Correlation Analysis for Decoding Multi-Modal Brain Development, Medical Image Computing and Computer-Assisted Intervention, Springer-Verlag, Lecture Notes in Computer Science, vol 12907, pp 400-409, 2021

• Ouyang et al.: Self-Supervised Longitudinal Neighbourhood Embedding, Medical Image Computing and Computer-Assisted Intervention, Springer-Verlag, Lecture Notes in Computer Science, vol 12902, pp 80-89, 2021.

• Lu M et al.: Quantifying Parkinson’s Disease Motor Severity Under Uncertainty Using MDS-UPDRS Videos, Medical Image Analysis, 2021

• Lu M et al.: Metadata Normalization, Conference on Computer Vision and Pattern Recognition, pp. 10917-10927, 2021.

• Zhao Q et al: Longitudinal Self-Supervised Learning, Medical Image Analysis, Volume 71, 102051, 2021.

• Liu Z et al: Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models, Information Processing in Medical Imaging, Lecture Notes in Computer Science,vol 12729, pp. 71-82, 2021.

• Ouyang J et al.: Representation Disentanglement for Multi-modal MR Analysis, Information Processing in Medical Imaging, Lecture Notes in Computer Science, vol 12729, pp. 321-333, 2021.

• Adeli E, et al.: Representation Learning with Statistical Independence to Mitigate Bias, 2020 Winter Conference on Applications of Computer Vision, IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2513-2523, 2021.

• Adeli, E et al.: Deep Learning Identifies Morphological Determinants of Sex Differences in the Pre-Adolescent Brain, NeuroImage, 223, 117293, 2020. [Additional Code]

• Gadgil S et al.: Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis, Medical Image Computing and Computer Assisted Intervention, Springer, Lecture Notes in Computer Science, vol 12267, pp 528-538, 2020.

• Lu M et al.: Vision-based Estimation of MDS-UPDRSGait Scores for Assessing Parkinson’s Disease Motor Severity, Medical Image Computing and Computer Assisted Intervention, Springer, Lecture Notes in Computer Science, 12263, pp 637-647, 2020.

• Ayub R et al.: Inpainting Cropped Diffusion MRI using Deep Generative Models, Predictive Intelligence in Medicine, Springer, Lecture Notes in Computer Science, 12329, pp 91-100, 2020.

• Adeli et al.: Logistic Regression Confined by Cardinality-Constrained Sample and Feature Selection, IEEE Transactions on Pattern Analysis And Machine Intelligence, 2020

• Adeli et al.: Novel Machine Learning Identifies Brain Patterns Distinguishing Diagnostic Membership of Human Immunodeficiency Virus, Alcoholism, and Their Comorbidity of Individuals, Biological Psychiatry: CNNI, 2019

• Zhao et al.: Confounder-Aware Visualization of ConvNets, International Workshop on Machine Learning in Medical Imaging, 2019

• Zhao et al.: Variational autoencoder with truncated mixture of Gaussians for functional connectivity analysis, International Conference on Information Processing in Medical Imaging, 2019

• Zhao et al.: Variational Autoencoder for Regression: Application to Brain Aging Analysis, Medical Image Computing and Computer-Assisted Intervention, Springer-Verlag, Lecture Notes in Computer Science, pp 823-31, 2019.

• Zhao et al.: Longitudinally Consistent Estimates of Intrinsic Functional Networks , Human Brain Mapping, 2019

• Adeli et al.: Chained Regularization for Identifying Brain Patterns Specific to HIV Infection , NeuroImage, 2018

• Adeli et al.: Multi-Label Transduction for Identifying Disease Comorbidity Patterns , Medical Image Computing and Computer Assisted Intervention, 2018

• Zhao et al.: A Riemannian Framework for Longitudinal Analysis of Resting-State Functional Connectivity , International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018

Software Packages

• MIQA
  Leveraging modern UI/UX and deep learning techniques for quality control of medical images

• Scalable Informatics for Biomedical Imaging Studies (SIBIS)
  SIBIS consists of IT infrastructure for uploading behavioral and imaging data through application programming interfaces to a central biomedical data repository,querying the data through a web interface, a validated workflow to perform qualitycontrol, and a multi-modal image processing pipeline.

• Sviewer
  3D+t viewer based on 3D Slicer technology

• BASIS
  Development environment accompanying tools for testing and packaging software across platforms and languages

• AtlasCreator
  Automatically extracts cohort specific data from set of training images

• GLISTR
  First automatic tool for segmenting glioma and healthy tissue from MR brain scans

• SceneView
  Graphical browser for scenes saved in 3D Slicer
• Annotation
  A tool for annotating medical scans using state-of-the-art 2D and 3D
• Change Tracker
  Semi-automatic tool for quantification of the subtle changes in pathology
• EMSegmenter
  An advanced MRI segmentation tool