E.Kim, C. Onweller, K. McCoy, “Information Graphic Summarization using a Collection of Multimodal Deep Neural Networks”, In International Conference on Pattern Recognition, ICPR, 2020 PDF
D. Schwartz, Y. Alparslan, E. Kim, “Regularization and Sparsity for Adversarial Robustness and Stable Attribution”, In the International Symposium on Visual Computing, ISVC 2020 PDF
J. Carter, J. Rego, D. Schwartz, V. Bhandawat, and E. Kim. ”Learning Spiking Neural Network Models of Drosophila Olfaction.” In International Conference on Neuromorphic Systems, ICONS pp. 1-5. 2020 PDF.
E.Kim, J. Rego, Y. Watkins, G. Kenyon, “Modeling Biological Immunity to Adversarial Examples”, In IEEE Computer Society Conf. Computer Vision and Pattern Recognition, CVPR 2020 PDF
Y. Watkins, E.Kim, A. Sornborger, G. Kenyon, “Using Sinusoidally-Modulated Noise as a Surrogate for Slow-Wave Sleep to Accomplish Stable Unsupervised Dictionary Learning in a Spike-Based Sparse Coding Models”, In IEEE Computer Society Conf. Computer Vision and Pattern Recognition Workshops, CVPR-W 2020 PDF.
J. Marharjan, B. Mitchell, V.W.S. Chan, E.Kim,“Guided Ultrasound Imaging using a Deep Regression Network”, In Ultrasonic Imaging and Tomography, SPIE Medical Imaging, 2020 PDF.
E.Kim, J.Yarnall, P.Shah, G.Kenyon, “A Neuromorphic Sparse Coding Defense to Adversarial Images”, International Conference on Neuromorphic Systems, ICONS, 2019 PDF.
E.Kim, E.Lawson, K.Sullivan, G.Kenyon, “Spatiotemporal Sequence Memory for Prediction using Deep Sparse Coding”, Neuro-inspired Computational Elements Workshop, NICE, 2019 PDF
J.Springer, C.Strauss, A.Thresher, E.Kim, G.Kenyon, “Classifiers Based on Deep Sparse Coding Architectures are Robust to Deep Learning Transferable Examples”, arXiv:1811.07211, 2018 PDF.
E.Kim, K.McCoy, “Multimodal Deep Learning using Images and Text for Information Graphic Classification”, ACM SIGACCESS Conference on Computers and Accessibility, Assets, 2018. (Best Paper Nominee) PDF
E.Kim, D.Hannan, G.Kenyon, “Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons”, International Conference on Computer Vision and Pattern Recognition, CVPR, 2018. PDF
E.Kim, S. Mente, A. Keenan, V. Gehlot,“Digital Pathology Data for Improved Deep Neural Network Classification”, In Imaging Informatics for Healthcare, Research, and Applications, SPIE Medical Imaging, 2017 PDF.
E.Kim, C.Moritz,“Enhancing the Communication Spectrum in Collaborative Virtual Environments”, In 12th International Symposium on Visual Computing, ISVC, 2016 PDF.
E.Kim, S. Vangala,“Deep Action Unit Classification using a Binned Intensity Loss and Semantic Context Model”, In 23rd International Conference on Pattern Recognition, ICPR, 2016 PDF.
E.Kim, S. Vangala,“Vinereactor: Crowdsourced Spontaneous Facial Expression Data”, In International Conference on Multimedia Retrieval, ICMR, 2016 PDF.
E.Kim, M. Corte-Real, Z.Baloch,“A Deep Semantic Mobile Application for Thyroid Cytopathololgy”, In SPIE Medical Imaging 2016: Advanced-PACS-based Imaging Informatics and Therapeutic Applications, 2016 PDF.
T.Xu, E.Kim, and X.Huang, “Adjustable AdaBoost Classifier and Pyramid Features for Image-based Cervical Cancer Diagnosis”, In International Symposium on Biomedical Imaging, ISBI 2015 PDF.
E.Kim, Z.Baloch, C.Kim, “Computer Assisted Detection and Analysis of Tall Cell Variant Papillary Thyroid Carcinoma in Histological Images”, In SPIE Medical Imaging 2015: Digital Pathology, 2015 PDF
T.Xu, X.Huang, E.Kim, L. Rodney Long, S.Antani, “Multi-test Cervical Cancer Diagnosis with Missing Data Estimation”, In SPIE Medical Imaging 2015: Computer Aided Diagnosis, 2015 PDF.
S.Bouloutian and E.Kim, “Artificial Intelligence Gaming Assistant for Google Glass”, In International Symposium on Visual Computing, ISVC, 2014 PDF.
E.Kim, H.Li, and X.Huang, “A Hierarchical Image Clustering Cosegmentation Framework”, In IEEE Computer Society Conf. Computer Vision and Pattern Recognition, CVPR 2012 PDF.
T.Shen, X.Huang, H.Li, E.Kim, S.Zhang, and J.Huang, “A 3D Laplacian-Driven Parametric Deformable Model”, In IEEE International Conference on Computer Vision, ICCV 2011 PDF.
E.Kim, X.Huang, and J.Heflin, “Finding VIPS - A Visual Image Persons Search Using A Content Property Reasoner and Web Ontology”, In IEEE International Conference on Multimedia & Expo, ICME 2011 PDF.
E.Kim, S.Antani, X.Huang, L.R.Long, and D.Demner-Fushman, “Using Relevant Regions in Image Search and Query Refinement for Medical CBIR”, In SPIE Med- ical Imaging 2011: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 2011 PDF.
E.Kim, T.Shen, and X.Huang, “A Parallel Cellular Automata with Label Priors for Interactive Brain Tumor Segmentation”, In The 23RD IEEE International Sym- posium on Computer-Based Medical Systems, CBMS 2010 PDF.
H.Li, E.Kim, X.Huang, and L.He, “Object Matching with a Locally Affine-Invariant Constraint”, In IEEE Computer Society Conf. Computer Vision and Pattern Recognition, CVPR 2010 PDF.
E.Kim, X.Huang, G.Tan, L.R.Long, and S.Antani, “A hierarchical SVG image abstraction layer for medical imaging”, In SPIE Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 2010 PDF.
E.Kim, W.Wang, H.Li, and X.Huang, “A Parallel Annealing Method For Automatic Color Cervigram Image Segmentation”, In Medical Image Computing and Computer Assisted Intervention, MICCAI-GRID, 2009 PDF.
T. Xu, H. Zhang, C. Xin, E.Kim, L.R. Long, Z. Xue, S. Antani, X. Huang, “Multi- feature Based Benchmark for Cervical Dysplasia Classification Evaluation”, Pattern Recognition, Sept. 2016.
J. Park, E.Kim, R. Werner, “Inpatient Hospital Charge Variability of U.S. Hospitals”, Journal of Internal Medicine, May 2015 PDF.
D.Song, E.Kim, X.Huang, J.Patruno, H.Munoz-Avila, J.Heflin, L. Rodney Long, S.Antani, “Multi-modal Entity Coreference for Cervical Dysplasia Diagnosis”, In IEEE Transactions on Medical Imaging, Vol. 34, No. 1, pp.229-245, Jan. 2015 PDF.
E.Kim, X.Huang, G.Tan, “Markup SVG - An Online Content Aware Image Abstraction and Annotation Tool”, In IEEE Transactions on Multimedia, Vol. 13, Issue 5, Oct. 2011 PDF.
E.Kim and X. Huang, “A Data Driven Approach to Cervigram Image Analysis and Classification”, In Color Medical Image Analysis, Lecture Notes in Computational Vision and Biomechanics, Volume 6, 2013. DOI: 10.1007/978-94-007-5389-1 1 PDF
Published in International Conference on Neuromorphic Systems, ICONS, 2020
We present research in the modeling of neurons within Drosophila (fruit fly) olfaction. We describe the process from data collection, to model creation, and spike generation. Our approach utilizes com- putational elements such as spiking neural networks that employ leaky integrate-and-fire neurons with adaptive firing behavior that more closely mimick biological neurons. We describe the methods of several learning implementations in both software and hard- ware. Finally, we present both quantitative and qualitative results on learning spiking neural network models.
Published in IEEE Computer Vision and Pattern Recognition, CVPR, 2020
In a general sense, adversarial attack through perturbations is not a machine learning vulnerability. Human and biological vision can also be fooled by various methods, i.e. mixing high and low frequency images together, by altering semantically related signals, or by sufficiently distorting the input signal. However, the amount and magnitude of such a distortion required to alter biological perception is at a much larger scale. In this work, we explored this gap through the lens of biology and neuroscience in order to understand the robustness exhibited in human perception. Our experiments show that by leveraging sparsity and modeling the biological mechanisms at a cellular level, we are able to mitigate the effect of adversarial alterations to the signal that have no perceptible meaning. Furthermore, we present and illustrate the effects of top-down functional processes that contribute to the inherent immunity in human perception in the context of exploiting these properties to make a more robust machine vision system.
Published in IEEE Computer Vision and Pattern Recognition Workshops, CVPRW, 2020
Sparse coding algorithms have been used to model the acquisition of V1 simple cell receptive fields as well as to accomplish the unsupervised acquisition of features for a variety of machine learning applications. The Locally Com- petitive Algorithm (LCA) provides a biologically plausible implementation of sparse coding based on lateral inhibi- tion. LCA can be reformulated to support dictionary learn- ing via an online local Hebbian rule that reduces predictive coding error. Although originally formulated in terms of leaky integrator rate-coded neurons, LCA based on lateral inhibition between leaky integrate-and-fire (LIF) neurons has been implemented on spiking neuromorphic processors but such implementations preclude local online learning. We previously reported that spiking LCA can be expressed in terms of predictive coding error in a manner that allows for unsupervised dictionary learning via a local Hebbian rule but the issue of stability has not previously been ad- dressed.
Published in Ultrasonic Imaging and Tomography, SPIE Medical Imaging, 2020
In this work, we present a machine learning method to guide an ultrasound operator towards a selected area of interest. Unlike other automatic medical imaging methods, ultrasound imaging is one of the few imaging modalities where the operator’s skill and training are critical in obtaining high quality images. Additionally, due to recent advances in affordability and portability of ultrasound technology, its utilization by non-experts has increased. Thus, there is a growing need for intelligent systems that have the ability to assist ultrasound operators in both clinical and non-clinical scenarios. We propose a system that leverages machine learning to map real time ultrasound scans to transformation vectors that can guide a user to a target organ or anatomical structure. We present a unique training system that passively collects supervised training data from an expert sonographer and uses this data to train a deep regression network. Our results show that we are able to recognize anatomical structure through the use of ultrasound imaging and give the user guidance toward obtaining an ideal image.