Peter Bajcsy

Publications

Journal publications:

Mathias Hammer, Maximiliaan Huisman, Alessandro Rigano, Ulrike Boehm, James J. Chambers, Nathalie Gaudreault, Alison J. North, Jaime A. Pimentel, Damir Sudar, Peter Bajcsy, Claire M. Brown, Alexander D. Corbett, Orestis Faklaris, Judith Lacoste, Alex Laude, Glyn Nelson, Roland Nitschke, Farzin Farzam, Carlas Smith, David Grunwald, Caterina Strambio-De-Castillia, “Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model,” Nature Methods, December (2021); https://www.nature.com/articles/s41592-021-01327-9

Ulrike Boehm, Glyn Nelson, Claire M. Brown, Steve Bagley, Peter Bajcsy, Johanna Bischof, Aurelien Dauphin, Ian M. Dobbie, John E. Eriksson, Orestis Faklaris, Julia Fernandez-Rodriguez, Alexia Ferrand, Laurent Gelman, Ali Gheisari, Hella Hartmann, Christian Kukat, Alex Laude, Miso Mitkovski, Sebastian Munck, Alison J. North, Tobias M. Rasse, Ute Resch-Genger, Lucas C. Schuetz, Arne Seitz, Caterina Strambio-De-Castillia, Jason R. Swedlow & Roland Nitschke, “QUAREP-LiMi: A community-driven initiative to establish guidelines for quality assessment and reproducibility for instruments and images in light microscopy,” Nature Methods, May (2021). https://doi.org/10.1038/s41592-021-01162-y, DOI: https://doi.org/10.1038/s41592-021-01162-y

Peter Bajcsy, Nicholas J. Schaub, and Michael Majurski, “Designing Trojan Detectors in Neural Networks Using Interactive Simulations,” Special Issue on Machine Learning for Cybersecurity Threats, Challenges, and Opportunities, Computing and Artificial Intelligence Section, Appl. Sci. 2021, 11, 0. https://doi.org/10.3390/app11010000; URL: https://www.mdpi.com/journal/applsci/special_issues/cybersecurity_threa…

Peter Bajcsy, Steve Feldman, Michael Majurski, Kenneth Snyder, and Mary Brady, “Approaches to Training AI-based Multi-class Semantic Image Segmentation,” Journal of Microscopy, 2020 May 14. doi: http://dx.doi.org/10.1111/jmi.12906; URL: https://pubmed.ncbi.nlm.nih.gov/32406521/;

Sarala Padi, Petre Manescu, Nicholas Schaub, Nathan Hotaling, Carl Simon, Jr., Kapil Bharti, and Peter Bajcsy, “Comparison of Artificial Intelligence Based Approaches to Cell Function Prediction”, Elsevier Journal of Informatics in Medicine Unlocked, Vol. 18, Available online 23 November 2019, 100270; https://doi.org/10.1016/j.imu.2019.100270; (Impact Factor: 2.11), https://www.sciencedirect.com/science/article/pii/S2352914819303235?via%3Dihub

Nicholas J. Schaub, Nathan A. Hotaling, Sarala Padi, Petre Manescu, Qin Wan, Ruchi Sharma, Joe Chalfoun, Mylene Simon, Mohamed Ouladi, Carl G. Simon, Jr., Peter Bajcsy, Kapil Bharti, “Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy,” Journal of Clinical Investigation. In-press preview published online November 14, 2019. DOI: 10.1172/JCI131187 (Impact: 12.28)

Sowon Yoon, Peter Bajcsy, Maritoni Litorja, James Filliben, “Evaluation of Lateral Resolution of Light Field Cameras,” Journal of Optical Engineering 57(9), 093101 (8 September 2018). https://doi.org/10.1117/1.OE.57.9.093101

Beatriz A. Pazmino Betancourt, Stephen J. Florczyk, Mylene Simon, Derek Juba, Jack F. Douglas, Walid Keyrouz, Peter Bajcsy, Christopher Lee and Carl G. Simon, Jr., “Effect of the Scaffold Microenvironment on Cell Polarizability and Capacitance Determined by Probabilistic Computations,” Biomedical Materials, BMM-101918.R1, Vol. 13, No. 2, pages 025012, Jan. 2018; URL: http://iopscience.iop.org/article/10.1088/1748-605X/aa9650

Books and Book Chapters:

P, Bajcsy, J. Chalfoun, M. Simon, M. Kociolek, and M. Brady, “Chapter 7: Object Measurements from 2D Microscopy Images,” in the book entitled “Modern Computer Vision for Microscopy Image Analysis” edited by Mei Chen, published by Elsevier Academic Press, December 11, 2020, ISBN: 9780128149720

P. Bajcsy, J. Chalfoun, M. Simon, “Web Microanalysis of Big Image Data,” Springer-Verlag International, February 2018, p. 197.

P. Bajcsy, K. McHenry, and R. Kooper, "Interaction between Objects Born in Digital and Physical Spaces," in in Pervasive Adaptation book on the Next Generation Pervasive Computing Research Agenda, A. Ferscha, Ed. Seattle, Washington: A book by Th. Sc. Community, Johannes Kepler University Linz, 2011, p. 80.

W. McFadden, R. Kooper, S.-C. Lee, and P. Bajcsy, "Comprehensive and Scalable Appraisals of Contemporary Documents," in in Applications of Machine Learning, Y. Zhang, Ed. IN-TECH, 2010, pp. 87–108.

P. Bajcsy and R. Kooper, "Integration of Data Across Disparate Sensing Systems Over Both Time and Space to Design Smart Environments," in in Sustainable Radio Frequency Identification Solutions, no. 1, C. Turcu, Ed. IN-TECH, 2010, pp. 281–306.

Conference Papers:

Victoria H. DiStefano, Jacob M. LaManna, David L. Jacobson, Paul A. Kienzle, Daniel S. Hussey, and Peter Bajcsy, “Assessment of Dose Reduction Strategies in Wavelength-Selective Neutron Tomography,” The 2^nd International Conference on Image Processing and Vision Engineering (IMPROVE 2022), 22-24 April 2022; On-line streaming URL: https://improve.scitevents.org/ (selected for the best paper award)

Mylene Simon, Nicholas Schaub, Sunny Yu, Jayapriya Nagarajan, Mohamed Ouladi, Sudharsan Prativadi Bayankaram, Peter Bajcsy and Nathan Hotaling, “Quantifying Variability in Microscopy Image Analyses for COVID-19 Drug Discovery,” 6th IEEE Workshop on Computer Vision for Microscopy Image Analysis (CVMI) at CVPR 2021, June 25, 2021

Michael Majurski, Derek Juba, Tim Blattner, Peter Bajcsy, Alden Dima, Walid Keyrouz, “Trojan Detection Evaluation: Finding Hidden Behavior in AI Models,” NVIDIA GTC conference, Cybersecurity session, October 2020, URL: https://www.nvidia.com/en-us/on-demand/session/gtcfall20-a21714/

Peter Bajcsy and Michael Majurski, “Baseline Pruning-Based Approach to Trojan Detection in Neural Networks, “ Arxiv, eprint 2101.12016, January 2021; URL: https://arxiv.org/abs/2101.12016

Peter Bajcsy and Michael Majurski, “Baseline Pruning-Based Approach to Trojan Detection in Neural Networks, “ Security and Safety in Machine Learning Systems Workshop at ICLR 2021, URL: https://aisecure-workshop.github.io/aml-iclr2021/cfp (oral presentation), May 7^th, 2021

Peter Bajcsy, Nicholas Schaub, and Michael Majurski, “Scientific Calculator for Designing Trojan Detectors in Neural Networks, “Association for the Advancement of Artificial Intelligence (AAAI), Fall Symposium Series (FSS), AI in Government and Public Sector Applications, Washington, DC, November 11–-14, 2020 (accepted oral) arXiv: arXiv:2006.03707v2 [cs.CR]; URL: https://arxiv.org/pdf/2006.03707.pdf

Peter Bajcsy and Nathan Hotaling, “Interoperability of Web Computational Plugins for Large Microscopy Image Analyses,” BioImage Informatics 2019 conference, Allen Institute, Seattle, WA, October 2-4, 2019 (oral)

Michael Majurski, Petru Manescu, Sarala Padi, Nicholas Schaub, Nathan Hotaling, Carl Simon Jr. and Peter Bajcsy, “Cell Image Segmentation using Generative Adversarial Networks, Transfer Learning, and Augmentations,” oral presentation, CVMI workshop at IEEE Computer Vision and Pattern Recognition conference, Long Beach, CA, June 17, 2019

Soweon Yoon, Peter Bajcsy, Litorja Maritoni, and James Filliben , “Evaluation of Lateral and Depth Resolutions of Light Field Camera,” M&M conference 2018, August 5-9, 2018, Baltimore, MD, (oral presentation)

E. Kwee, A. Peterson, J. Stinson, M. Halter, L. Yu, M. Majurski, J. Chalfoun, P. Bajcsy, and J. Elliott, “Large Field of View Quantitative Phase Imaging of Induced Pluripotent Stem Cells and Optical Pathlength Reference Materials,” Quantitative Phase Imaging IV, SPIE 2018, 28 - 30 January 2018.

N. Hotaling, M. Simon, M. Ouladi, N. Schaub, T. Uddin, P. Manickam, D. Ortolan, H. Zhao, J. Chalfoun, P. Bajcsy, and K. Bharti, “REShAPE – A Cloud-Computing Based Cell Morphometry Analyzer for Development of a Release Criteria of Clinical Grade Retinal Pigment Epithelium,” Invest. Ophthalmol. Vis. Sci. 2017;58(8):396; The 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

M. Simon, J. Chalfoun, M. Brady, and P. Bajcsy, “Do We Trust Image Measurements? Variability, Accuracy and Traceability of Image Features,” IEEE International Conference on Big Data, Washington DC, December 5-8, 2016.

P. Bajcsy, A. Vandecreme, and M. Brady, “Interactive Web-based Spatio-Statistical Image Modeling from Gigapixel Images to Improve Discovery and Traceability of Published Statistical Models,” Microscopy and Microanalyses, Columbus, Ohio. July 24-28, 2016.

M. Majurski, J. Chalfoun, S. Lund, P. Bajcsy, and M. Brady, "Methodology for Increasing Image Feature Measurement Accuracy", CVPR Workshop entitled “Computer Vision for Microscopy Image Analysis”, July 1, 2016, Las Vegas, Nevada.

Recent Presentations

2022

2021

[1] “Trusted data repositories for reproducible science,” A NITRD Big Data Interagency Working Group Virtual Workshop focused on Pioneering the Future of Federally Supported Data Repositories, January 13th - Friday Jan 15th, 2021

[2] “Approaches to Trojan Detection in Neural Networks,” Department of Electrical and Computer Engineering Seminar, George Washington University, School of Engineering and Applied Science, on February 22, 2021 (virtual)

[3 ] “Synergistic Projects Between Imaging and Image Analytics,” the Bioforum in the Biosystems and Biomaterials Division (BBD), Material and Measurements Laboratory (MML) at NIST, April 28^th, 2021 (virtual)

[4] “TrojAI Challenge Overview”, Panel 2: Overview of Past & Current Evaluations and Panel 3: Discussion of NIST/Community Future Work, AI Measurement and Evaluation workshop hosted at NIST, June 15-17, 2021; URL: https://www.nist.gov/news-events/events/2021/06/ai-measurement-and-evaluation-workshop (virtual)

[5] “Imaging”, National Academy of Sciences, Engineering, and Medicine (NASEM) Panel on Review of Selected Divisions of the National Institute of Standards and Technology (NIST), Information Technology Laboratory (ITL), Software and Systems Division, June 21–24, 2021 (virtual)

2020

[1] “Beyond High Content Screening: An Open Next Generation Image Analysis Platform,” oral presentation on Jan 29, at the track “Automation and High-Throughput Technologies” in the session “Open Source Automation” at the Society for Laboratory Automation and Screening (SLAS2020) conference, San Diego, CA, Jan 25-29, 2020 (jointly presented with Nathan Hotaling)

[2] “Interoperable Algorithmic Plugins for Large Microscopy Image Analyses,” 2020 LIMPID+IDEAS2 Joint Workshop On Scalable Image Informatics (LIMPID) and Applications of Machine Learning to Materials Discovery (IDEAS), February 3-5, 2020

[3] “Interoperability of Web Computational Plugins for Large Microscopy Image Analyses,” NIST Big Data Public Working Group (NBD-PWG), virtual, February 18^th, 2020

[4] “Artificial Intelligence for Image-Based Measurements,” Office of Data Informatics (ODI) seminar series, NIST Gaithersburg, MD, March 3rd, 2020

[5] “Machine Learning for Manufacturing Robotics,” Manipulation & Mobility Systems Group of the Intelligent Systems Division (ISD) at the National Institute of Standards and Technology, Gaithersburg, MD, May 22^nd, 2020

[6] “Collaborative Image Analysis in the Cloud for Improved Reproducibility and High Scalability,” International Society for Advancement in Cytometry (ISAC) webinar, December 10^th, 2020 (co-presented with NCATS NIH and Zeiss speakers)

Publications

Material Research Support for the Structural Performance of Nuclear Power Plant Concrete Structures Affected by Alkali-Silica Reaction

May 16, 2022

Author(s)

Kenneth A. Snyder, Richard Eason, Peter Bajcsy, Steven Feldman

Towards community-driven metadata standards for light microscopy: tiered guidelines extending the OME model

December 1, 2021

Author(s)

Peter Bajcsy, Mathias Hammer, Maximiliaan Huisman, Alex Rigano, Ulrike Boehm, James J. Chambers, Nathalie Gaudreault, Jaime A. Pimentel, Damir Sudar, Claire M. Brown, Alexander D. Corbett, Orestis Faklaris, Judith Lacoste, Alex Laude, Glyn Nelson, Roland Nitschke, Alison J. North, Renu Gopinathan, Farzin Farzam, Carlas Smith, David Grunwald, Caterina Strambio-De-Castillia

While the power of modern microscopy techniques is undeniable, rigorous record-keeping and quality control are required to ensure that imaging data may be

Exact Tile-Based Segmentation Inference for Images Larger than GPU Memory

June 3, 2021

Author(s)

Michael P. Majurski, Peter Bajcsy

We address the problem of performing exact (tiling-error free) out-of-core semantic segmentation inference of arbitrarily large images using fully convolutional

Designing Trojan Detectors in Neural Networks Using Interactive Simulations

February 20, 2021

Author(s)

Peter Bajcsy, Nicholas J. Schaub, Michael P. Majurski

This paper addresses the problem of designing trojan detectors in neural networks (NNs) using interactive simulations. Trojans in NNs are defined as triggers in

Approaches to Training Multi-Class Semantic Image Segmentation of Damage in Concrete

May 14, 2020

Author(s)

Peter Bajcsy, Steven B. Feldman, Michael P. Majurski, Kenneth A. Snyder, Mary C. Brady

This paper addresses the problem of creating a large quantity of high-quality training image segmentation masks from scanning electron microscopy (SEM) images

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Peter Bajcsy (Fed)

Publications

Recent Presentations

Quotes About My Work

Teaching Experience

Community Services

Awards

Publications

Material Research Support for the Structural Performance of Nuclear Power Plant Concrete Structures Affected by Alkali-Silica Reaction

Towards community-driven metadata standards for light microscopy: tiered guidelines extending the OME model

Exact Tile-Based Segmentation Inference for Images Larger than GPU Memory

Designing Trojan Detectors in Neural Networks Using Interactive Simulations

Approaches to Training Multi-Class Semantic Image Segmentation of Damage in Concrete