U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Search Publications by: Michael Paul Majurski (Fed)

Search Title, Abstract, Conference, Citation, Keyword or Author
Displaying 1 - 25 of 26

Measuring Dimensionality of Cell-Scaffold Contacts of Primary Human Bone Marrow Stromal Cells Cultured on Electrospun Fiber Scaffolds

January 1, 2023
Author(s)
Carl Simon Jr., Peter Bajcsy, Joe Chalfoun, Michael Paul Majurski, Mary C. Brady, Mylene Simon, Nathan Hotaling, Nick Schaub, Allison Horenberg, Piotr Szczypinski, Dongbo Wang, Veronica DeFelice, Soweon Yoon, Stephanie Florczyk
The properties and structure of the cellular microenvironment can influence cell behavior. Sites of cell adhesion to the extracellular matrix (ECM) initiate intracellular signaling that direct cell functions such as proliferation, differentiation and

Characterization of AI Model Configurations For Model Reuse

October 24, 2022
Author(s)
Peter Bajcsy, Daniel Gao, Michael Paul Majurski, Thomas Cleveland, Manuel Carrasco, Michael Buschmann, Walid Keyrouz
With the widespread creation of artificial intelligence (AI) models in biosciences, bio-medical researchers are reusing trained AI models from other applications. This work is motivated by the need to characterize trained AI models for reuse based on

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 neural networks (FCN). FCN models have the property that once a model is trained, it can be

Baseline Pruning-Based Approach to Trojan Detection in Neural Networks

May 7, 2021
Author(s)
Peter Bajcsy, Michael Paul Majurski
This paper addresses the problem of detecting trojans in neural networks (NNs) by analyzing how NN accuracy responds to systematic pruning. This study leverages the NN models generated for the TrojAI challenges. Our pruning-based approach (1) detects any

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 inputs that cause misclassification of such inputs into a class (or classes) unintended by the

Trojan Detection Evaluation: Finding Hidden Behavior in AI Models

October 10, 2020
Author(s)
Michael Paul Majurski, Derek Juba, Timothy Blattner, Peter Bajcsy, Walid Keyrouz
Neural Networks are trained on data, learn relationships in that data, and then are deployed to the world to operate on new data. For example, a traffic sign classification AI can differentiate stop signs and speed limit signs. One potential problem is

Detection of Dense, Overlapping, Geometric Objects

July 1, 2020
Author(s)
Adele P. Peskin, Boris Wilthan, Michael P. Majurski
Using a unique data collection, we are able to study the detection of dense geometric objects in image data where object density, clarity, and size vary. The data is a large set of black and white images of scatterplots, taken from journals reporting

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 of concrete samples that exhibit progressive amounts of degradation resulting from alkali-silica

Summary: Workshop on Machine Learning for Optical Communication Systems

March 26, 2020
Author(s)
Joshua A. Gordon, Abdella Battou, Michael P. Majurski, Dan Kilper, Uiara Celine, Massimo Tonatore, Joao Pedro, Jesse Simsarian, Jim Westdorp, Darko Zibar
Optical communication systems are expected to find use in new applications that require more intelligent and automated functionality. Optical networks are needed to address the high speeds and low latency of 5G wireless networks. The analog nature of

Large Field of View Quantitative Phase Imaging of Induced Pluripotent Stem Cells and Optical Pathlength Reference Materials

February 23, 2018
Author(s)
Edward J. Kwee, Alexander W. Peterson, Jeffrey R. Stinson, Michael W. Halter, Liya Yu, Michael P. Majurski, Joe Chalfoun, Peter Bajcsy, John T. Elliott
Induced pluripotent stem cells (iPSCs) are reprogrammed cells that can have heterogeneous biological potential. Quality assurance metrics of reprogrammed iPSCs will be critical to ensure reliable use in cell therapies and personalized diagnostic tests. We

From Image Tiles to Web-Based Interactive Measurements in One Stop

January 4, 2017
Author(s)
Antoine Vandecreme, Michael P. Majurski, Joe Chalfoun, Keana Scott, John Henry Scott, Mary C. Brady, Peter Bajcsy
This article aims at introducing readers to a web-based solution useful for interactive measurements of centimeter-sized specimens at nanoscales. Modern imaging technology has enabled nanoscale imaging to become a routine process. As the imaging technology

Enabling Interactive Measurements from Large Coverage Microscopy

July 1, 2016
Author(s)
Peter Bajcsy, Antoine Vandecreme, Julien M. Amelot, Mary C. Brady, Joe Chalfoun, Michael P. Majurski
Microscopy could be an important tool for characterizing stem cell products if quantitative measurements could be collected over multiple spatial and temporal scales. With the cells changing states over time and being several orders of magnitude smaller

Methodology for Increasing Image Feature Measurement Accuracy

June 30, 2016
Author(s)
Michael Paul Majurski, Joe Chalfoun, Steven Lund, Peter Bajcsy, Mary C. Brady
Motivation Image features are computed in cell biology to derive quantitative information regarding cell state, differentiation, biological activity, and cell dynamics. The accuracy of any biological conclusions depends on the accuracy of the measured

Survey Statistics of Automated Segmentations Applied to Optical Imaging of Mammalian Cells

January 8, 2016
Author(s)
Peter Bajcsy, Antonio Cardone, Joe Chalfoun, Michael W. Halter, Derek Juba, Marcin Kociolek, Michael P. Majurski, Adele P. Peskin, Carl G. Simon Jr., Mylene H. Simon, Antoine Vandecreme, Anne L. Plant, Mary C. Brady
The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are

Shape Descriptors Comparison for Cell Tracking

October 15, 2015
Author(s)
Michael P. Majurski, Christopher Zheng, Joe Chalfoun, Alden A. Dima, Mary C. Brady
New microscope technologies are enabling the acquisition of large volumes of live cell image data. Accurate temporal object tracking is required to facilitate the analysis of this data. One principle component of cell tracking is correspondence, matching