Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Michael Majurski"'
Autor:
Joe Chalfoun, Michael Majurski, Tim Blattner, Kiran Bhadriraju, Walid Keyrouz, Peter Bajcsy, Mary Brady
Publikováno v:
Scientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
Abstract Automated microscopy can image specimens larger than the microscope’s field of view (FOV) by stitching overlapping image tiles. It also enables time-lapse studies of entire cell cultures in multiple imaging modalities. We created MIST (Mic
Externí odkaz:
https://doaj.org/article/4afd7ffa4275492fa81c5fd48e507792
Publikováno v:
Applied Sciences, Vol 11, Iss 4, p 1865 (2021)
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 b
Externí odkaz:
https://doaj.org/article/57681294fa304564a1a1a4e0b4feef32
Autor:
Peter Bajcsy, Michael Majurski
Publikováno v:
Journal of Research of the National Institute of Standards and Technology. 126
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
Publikováno v:
CVPR Workshops
We quantify the robustness of the semantic segmentation model U-Net, applied to single cell nuclei detection, with respect to the following factors: (1) automated vs manual training annotations, (2) quantity of training data, and (3) microscope image
Autor:
Josh Gordon, Jesse E. Simsarian, Uiara Celine, Daniel C. Kilper, Michael Majurski, Jim Westdorp, Abdella Battou, Massimo Tornatore, Darko Zibar, Joao Pedro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::00e199b1804a85bb5959f2bfa0cec25f
https://doi.org/10.6028/nist.sp.2100-04
https://doi.org/10.6028/nist.sp.2100-04
Publikováno v:
J Microsc
This paper addresses the problem of creating a large quantity of high-quality training segmentation masks from scanning electron microscopy (SEM) images. The images are acquired from concrete samples that exhibit progressive amounts of degradation re
Publikováno v:
Int J Artif Intell Appl
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a265feec49e6e97b8728940b397dca9
https://europepmc.org/articles/PMC8191419/
https://europepmc.org/articles/PMC8191419/
Autor:
Carl G. Simon, Petru Manescu, Nathan Hotaling, Sarala Padi, Peter Bajcsy, Nicholas J. Schaub, Michael Majurski
Publikováno v:
CVPR Workshops
We address the problem of segmenting cell contours from microscopy images of human induced pluripotent Retinal Pigment Epithelial stem cells (iRPE) using Convolutional Neural Networks (CNN). Our goal is to compare the accuracy gains of CNN-based segm
Publikováno v:
Computer. 49:70-79
Microscopes can now cover large spatial areas and capture stem cell behavior over time. However, without discovering statistically reliable quantitative stem cell quality measures, products cannot be released to market. A Web-based measurement system
Autor:
Edward Kwee, Joe Chalfoun, Peter Bajcsy, Michael Halter, Alexander W. Peterson, Liya Yu, John T. Elliott, Jeffrey Stinson, Michael Majurski
Publikováno v:
Quantitative Phase Imaging IV.
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 test