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Search Publications by: Peter Bajcsy (Fed)

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Displaying 1 - 25 of 71

AI driven 3D subcellular RPE map discovers cell state transitions in establishment of apical-basal polarity

January 12, 2026
Author(s)
Davide Ortolan, Pushkar Sathe, Andrei Volkov, Dominik Reichert, Sheldon Sebastian, Arvydas Maminishkis, Nicolas Schaub, Devika Bose, Jorge Alejandro Suarez de Freitas Ferrari, Nyusha Lin, Gianluca Pegoraro, Carl Simon, Ruchi Sharma, Peter Bajcsy, Kapil Bharti
The retinal pigment epithelium (RPE) is a specialized cell monolayer that forms the barrier between the subretinal and choroidal spaces. During development, RPE cells polarize perpendicular to the monolayer plane such that organelles attain specific

Relating Human and AI-based Detection Limits in SEM Dimensional Metrology

October 25, 2025
Author(s)
Peter Bajcsy, Pushkar Sathe, Andras Vladar
Background: The nanoscale measurements of critical dimensions in semiconductor manufacturing rely on scanning electron microscopy (SEM) and SEM image analyses. The acquisition of SEM images requires a low primary electron beam current and a low dose of the

Pixel Latency Measurements of Event Cameras

October 21, 2025
Author(s)
Remi Chassagnol, Timothy Blattner, Maritoni Litorja, Peter Bajcsy
Pixel latency of event cameras is an important parameter to take into account in real-time applications. Currently, pixel latency is insufficiently described in event camera specifications as a typical latency at a certain light level. This work is

Explaining poisoned AI models

June 11, 2025
Author(s)
Peter Bajcsy, Antonio Cardone, Philippe Dessauw, Chenyi Ling, Michael Majurski, Timothy Blattner, Derek Juba, Walid Keyrouz
This work presents a hierarchical approach to explaining poisoned artificial intelligence (AI) models. The motivation comes from using AI models in security and safety-critical applications, for instance, AI models for classifying road traffic signs in

Detection limits of AI-based SEM dimensional metrology

March 14, 2025
Author(s)
Peter Bajcsy, Brycie Wiseman, Michael Paul Majurski, Andras Vladar
The speed of in-line scanning electron microscope (SEM) measurements of linewidth, contact hole, and overlay is critically important for identifying the measurement area and generating indispensable process control information. Sample charging and damage

Enabling Global Image Data Sharing in the Life Sciences

December 23, 2024
Author(s)
Anne L. Plant, Peter Bajcsy
Public, reference data are one of the most important foundational resources in the modern life and biomedical sciences. Over the last 40 years, the public release and availability of genomic and macromolecule structural databases accelerated discovery

Small-Angle Scattering and Dark-Field Imaging for Validation of a New Neutron Far-Field Interferometer

December 1, 2024
Author(s)
Caitlyn M. Wolf, Peter Bajcsy, Wei-Ren Chen, Robert Dalgliesh, Michael Daugherty, Liliana de Campo, Fumiaki Funama, Lilin He, Michael Huber, David Jacobson, Paul A. Kienzle, Youngju Kim, Hubert King, Nikolai Klimov, Jacob LaManna, Fankang Li, Alexander Long, Ryan Murphy, Gergely Nagy, Sarah M. Robinson, Pushkar Sathe, Gregory N. Smith, Anna Sokolova, Sven Vogel, Erik Watkins, Yuxuan Zhang, Daniel Hussey, Kathleen Weigandt
The continued advancement of complex materials often requires a deeper understanding of the structure–function relationship across many length scales, which quickly becomes an arduous task when multiple measurements are required to characterize

Interactive Simulations of Backdoors in Neural Networks

May 21, 2024
Author(s)
Peter Bajcsy, Maxime Bros, Matthew Coudron
This work addresses the problem of planting and defending cryptographic-based backdoors in artificial intelligence models. The motivation comes from our lack of understanding and the implications of using cryptographic techniques for planting undetectable

Simulation of Neutron Dark-Field Data for Grating-Based Interferometers

April 1, 2024
Author(s)
Caitlyn Wolf, Youngju Kim, Paul A. Kienzle, Pushkar Sathe, Michael Daugherty, Peter Bajcsy, Daniel Hussey, Kathleen Weigandt
Hierarchical structures and heterogeneous materials are found in many natural and engineered systems including additive manufacturing, alternative energy, biology and polymer science. Though the structure–function relationship is important for developing

Data-driven Simulations For Training AI-Based Segmentation of Neutron Images

March 19, 2024
Author(s)
Pushkar Sathe, Caitlyn M. Wolf, Youngju Kim, Sarah M. Robinson, Michael Daugherty, Ryan Murphy, Jacob LaManna, Michael Huber, David Jacobson, Paul A. Kienzle, Kathleen Weigandt, Nikolai Klimov, Daniel Hussey, Peter Bajcsy
Neutron interferometry is unique in its ability to measure atomic scale properties of materials that no other imaging modality can. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on

Assessment of Dose Reduction Strategies in Wavelength-Selective Neutron Tomography

July 31, 2023
Author(s)
Daniel Hussey, Peter Bajcsy, Paul A. Kienzle, Jacob LaManna, David Jacobson, Victoria DiStefano, Daniel Pelt, James Sethian
The goal of this study is to determine variable relationships and a computational workflow that yield the highest quality of three-dimensional reconstructions in neutron imaging applications with reduced number of projections angles. Neutrons interact with

Three-dimensional, label-free cell viability measurements in tissue engineering scaffolds using optical coherence tomography

March 14, 2023
Author(s)
Greta Babakhanova, Anant Agrawal, Deepika Arora, Allison Horenberg, Jagat Budhathoki, Joy Dunkers, Joe Chalfoun, Peter Bajcsy, Carl Simon Jr.
In the field of tissue engineering, 3D scaffolds and cells are often combined to yield constructs that are used as therapeutics to repair or restore tissue function in patients. Viable cells are required to achieve the intended mechanism of action for the

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

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 properly interpreted (quality), reproduced (reproducibility), and used to extract reliable

Quantifying Variability in Microscopy Image Analyses for COVID-19 Drug Discovery

June 25, 2021
Author(s)
Peter Bajcsy, Mylene Simon, Sunny Yu, Nick Schaub, Jayapriya Nagarajan, Sudharsan Prativadi, Mohamed Ouladi, Nathan Hotaling
Microscopy image-based measurement variability in high-throughput imaging experiments for biological drug discoveries, such as COVID-19 therapies was addressed in this study. Variability of measurements came from (1) computational approaches (methods), (2)

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 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

Object Measurements from 2D Microscopy Images

December 11, 2020
Author(s)
Peter Bajcsy, Joe Chalfoun, Mylene Simon, Mary C. Brady, Marcin Kociolek
This chapter addresses object measurements from 2D microscopy images. Object measurements (called image features) vary in terms of theoretical formulas for the same image feature, the physical units used to represent pixel-based measurements, the

Trojan Detection Evaluation: Finding Hidden Behavior in AI Models

October 10, 2020
Author(s)
Michael 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
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