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Sibley, Txai; Holm, Elizabeth A.; Field, Kevin G. (2026) Evaluating and enhancing Segment Anything Model transferability for microstructural image analysis in nuclear materials. Computational Materials Science, 268. doi:10.1016/j.commatsci.2026.114620

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Reference TypeJournal (article/letter/editorial)
TitleEvaluating and enhancing Segment Anything Model transferability for microstructural image analysis in nuclear materials
JournalComputational Materials Science
AuthorsSibley, TxaiAuthor
Holm, Elizabeth A.Author
Field, Kevin G.Author
Year2026 (April)Volume268
PublisherElsevier BV
DOIdoi:10.1016/j.commatsci.2026.114620Search in ResearchGate
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Mindat Ref. ID19749044Long-form Identifiermindat:1:5:19749044:2
GUID0
Full ReferenceSibley, Txai; Holm, Elizabeth A.; Field, Kevin G. (2026) Evaluating and enhancing Segment Anything Model transferability for microstructural image analysis in nuclear materials. Computational Materials Science, 268. doi:10.1016/j.commatsci.2026.114620
Plain TextSibley, Txai; Holm, Elizabeth A.; Field, Kevin G. (2026) Evaluating and enhancing Segment Anything Model transferability for microstructural image analysis in nuclear materials. Computational Materials Science, 268. doi:10.1016/j.commatsci.2026.114620
In(2026) Computational Materials Science Vol. 268. Elsevier BV

References Listed

These are the references the publisher has listed as being connected to the article. Please check the article itself for the full list of references which may differ. Not all references are currently linkable within the Digital Library.

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Plank (2022)
Cho (2016)
Jacobs (2022) Cell Rep. Phys. Sci. Performance and limitations of deep learning semantic segmentation of multiple defects in transmission electron micrographs 3
Not Yet Imported: - journal-article : 10.1186/s40537-023-00727-2

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Kirillov (2023)
Li (2024)
Ma (2025) Scr. Mater. Alloy microstructure segmentation through SAM and domain knowledge without extra training 260
Grundler (2014) Power Plant Chem. Nobel metal chemical addition for mitigation of stress corrosion cracking: Theoretical insights and applications 16, 75
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Li (2025) Digit. Discov. Predicting performance of object detection models in electron microscopy using random forests 4, 987
Olamofe (2025) IEEE Access Performance evaluation of image super-resolution for cavity detection in irradiated materials 13, 68052
GitHub - facebookresearch/segment-anything: The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model., URL: https://github.com/facebookresearch/segment-anything.
(2025)
Christen (2023) ACM Comput. Surv. A review of the F-measure: Its history, properties, criticism, and alternatives 56, 73:1
M. Ruehle, Transmission Electron Microscopy of Radiation-Induced Defects, Technical Report CONF-710601–5, 4027809, 1971, http://dx.doi.org/10.2172/4027809, URL: http://www.osti.gov/servlets/purl/4027809-77bd7U/.
Xie (2025)
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OpenCV: Contour Features, URL: https://docs.opencv.org/4.x/dd/d49/tutorial_py_contour_features.html.
torchvision.ops.boxes — Torchvision main documentation, URL: https://docs.pytorch.org/vision/main/_modules/torchvision/ops/boxes.html#nms.
Grundler (2014) Power Plant Chem. Noble metal chemical addition for mitigation of stress corrosion cracking: Theoretical insights and applications 16, 76
Rowthu (2018)
SA-1B Dataset, URL: https://ai.meta.com/datasets/segment-anything.
Not Yet Imported: - journal-article : 10.1007/s10994-023-06326-9

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Not Yet Imported: Neural Networks - journal-article : 10.1016/j.neunet.2018.07.011

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Tsutsui (2022) Mater. Today Commun. Mixing effects of SEM imaging conditions on convolutional neural network-based low-carbon steel classification 32
Raiaan (2024) Decis. Anal. J. A systematic review of hyperparameter optimization techniques in Convolutional Neural Networks 11


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