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Search Publications by: Peter Bajcsy (Fed)

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Displaying 1 - 25 of 61

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

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 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

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 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

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

Quantitative Bright-Field Microscopy Combined with Deep Neural Networks Predict Live Tissue Function

February 29, 2020
Author(s)
Carl Simon Jr., Nicholas J. Schaub, Petru S. Manescu, Sarala Padi, Mylene Simon, Peter Bajcsy, Nathan A. Hotaling, Joe Chalfoun, Mohamed Ouladi, Qin Wan, Kapil Bharti, Ruchi Sharma
Progressive increases in the number of cell therapies in the preclinical and clinical phases has prompted the need for reliable and non-invasive assays to validate transplant function in clinical biomanufacturing. Here, we developed a robust

Comparison of Artificial Intelligence based approaches to cell function prediction

July 11, 2019
Author(s)
Sarala Padi, Petru S. Manescu, Nicholas Schaub, Nathan Hotaling, Carl G. Simon Jr., Peter Bajcsy
Predicting Retinal Pigment Epithelium (RPE) cell functions in stem cell implants using non-invasive bright field microscopy imaging is a critical task for clinical deployment of stem cell therapies. Such cell function predictions can be carried out either

Evaluation of Lateral Resolution of Light Field Cameras

September 8, 2018
Author(s)
Sowon Joy Yoon, Peter Bajcsy, Maritoni A. Litorja, James J. Filliben
Light field cameras are an emerging imaging device for acquiring 3-D information of a scene by capturing a field of light rays traveling in space. As light field cameras become portable, hand- held, and affordable, their potential as a 3-D measurement

Evaluation of Lateral and Depth Resolutions of Light Field Cameras

August 1, 2018
Author(s)
Sowon Joy Yoon, Peter Bajcsy, Maritoni A. Litorja, James J. Filliben
In crime scene investigations, 3D forensic evidence such as tire tread and shoe imprints in substances like mud or snow can often provide useful information to identify suspects and victims. This work focuses on evaluating lateral and depth resolutions of