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of 293
pro vyhledávání: '"P. Jewsbury"'
Stain normalization algorithms aim to transform the color and intensity characteristics of a source multi-gigapixel histology image to match those of a target image, mitigating inconsistencies in the appearance of stains used to highlight cellular co
Externí odkaz:
http://arxiv.org/abs/2403.09302
Autor:
Jahanifar, Mostafa, Raza, Manahil, Xu, Kesi, Vuong, Trinh, Jewsbury, Rob, Shephard, Adam, Zamanitajeddin, Neda, Kwak, Jin Tae, Raza, Shan E Ahmed, Minhas, Fayyaz, Rajpoot, Nasir
Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) by tackling intricate tasks across an array of histology image analysis applications. Nevertheless, the presence of out-of-distribution data (stemming fr
Externí odkaz:
http://arxiv.org/abs/2310.19656
Publikováno v:
Methodology, Vol 20, Iss 3, Pp 218-237 (2024)
In education testing, the items that examinees receive may be selected for a variety of reasons, resulting in missing data for items that were not selected. Item selection is internal when based on prior performance on the test, such as in adaptive t
Externí odkaz:
https://doaj.org/article/d553b11f4aa84326b7c3cd810d32134c
Publikováno v:
Large-scale Assessments in Education, Vol 12, Iss 1, Pp 1-27 (2024)
Abstract Large-scale assessments are rich sources of data that can inform a diverse range of research questions related to educational policy and practice. For this reason, datasets from large-scale assessments are available to enable secondary analy
Externí odkaz:
https://doaj.org/article/501dedd4043a439697756a8cd462f64d
Autor:
Vu, Quoc Dang, Jewsbury, Robert, Graham, Simon, Jahanifar, Mostafa, Raza, Shan E Ahmed, Minhas, Fayyaz, Bhalerao, Abhir, Rajpoot, Nasir
Since the introduction of digital and computational pathology as a field, one of the major problems in the clinical application of algorithms has been the struggle to generalize well to examples outside the distribution of the training data. Existing
Externí odkaz:
http://arxiv.org/abs/2301.03418
Autor:
Bilal, Mohsin, Jewsbury, Robert, Wang, Ruoyu, AlGhamdi, Hammam M., Asif, Amina, Eastwood, Mark, Rajpoot, Nasir
Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide images (WSIs) often process a large number of tiles (sub-images) and require aggregating predictions from the tiles in order to predict WSI-level labels. In this
Externí odkaz:
http://arxiv.org/abs/2211.01256
The field of computational pathology presents many challenges for computer vision algorithms due to the sheer size of pathology images. Histopathology images are large and need to be split up into image tiles or patches so modern convolutional neural
Externí odkaz:
http://arxiv.org/abs/2108.10873
Akademický článek
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Publikováno v:
Large-scale Assessments in Education, Vol 10, Iss 1, Pp 1-19 (2022)
Abstract The present paper investigates and examines the conditional dependencies between cognitive responses (RA; Response Accuracy) and process data, in particular, response times (RT) in large-scale educational assessments. Using two prominent lar
Externí odkaz:
https://doaj.org/article/37e200e933d1440391e2a5c7b70ad806
Autor:
Skoulidis, Ferdinandos, Araujo, Haniel A., Do, Minh Truong, Qian, Yu, Sun, Xin, Cobo, Ana Galan, Le, John T., Montesion, Meagan, Palmer, Rachael, Jahchan, Nadine, Juan, Joseph M., Min, Chengyin, Yu, Yi, Pan, Xuewen, Arbour, Kathryn C., Vokes, Natalie, Schmidt, Stephanie T., Molkentine, David, Owen, Dwight H., Memmott, Regan, Patil, Pradnya D., Marmarelis, Melina E., Awad, Mark M., Murray, Joseph C., Hellyer, Jessica A., Gainor, Justin F., Dimou, Anastasios, Bestvina, Christine M., Shu, Catherine A., Riess, Jonathan W., Blakely, Collin M., Pecot, Chad V., Mezquita, Laura, Tabbó, Fabrizio, Scheffler, Matthias, Digumarthy, Subba, Mooradian, Meghan J., Sacher, Adrian G., Lau, Sally C. M., Saltos, Andreas N., Rotow, Julia, Johnson, Rocio Perez, Liu, Corinne, Stewart, Tyler, Goldberg, Sarah B., Killam, Jonathan, Walther, Zenta, Schalper, Kurt, Davies, Kurtis D., Woodcock, Mark G., Anagnostou, Valsamo, Marrone, Kristen A., Forde, Patrick M., Ricciuti, Biagio, Venkatraman, Deepti, Van Allen, Eliezer M., Cummings, Amy L., Goldman, Jonathan W., Shaish, Hiram, Kier, Melanie, Katz, Sharyn, Aggarwal, Charu, Ni, Ying, Azok, Joseph T., Segal, Jeremy, Ritterhouse, Lauren, Neal, Joel W., Lacroix, Ludovic, Elamin, Yasir Y., Negrao, Marcelo V., Le, Xiuning, Lam, Vincent K., Lewis, Whitney E., Kemp, Haley N., Carter, Brett, Roth, Jack A., Swisher, Stephen, Lee, Richard, Zhou, Teng, Poteete, Alissa, Kong, Yifan, Takehara, Tomohiro, Paula, Alvaro Guimaraes, Parra Cuentas, Edwin R., Behrens, Carmen, Wistuba, Ignacio I., Zhang, Jianjun, Blumenschein, George R., Gay, Carl, Byers, Lauren A., Gibbons, Don L., Tsao, Anne, Lee, J. Jack, Bivona, Trever G., Camidge, D. Ross, Gray, Jhannelle E., Lieghl, Natasha, Levy, Benjamin, Brahmer, Julie R., Garassino, Marina C., Gandara, David R., Garon, Edward B., Rizvi, Naiyer A., Scagliotti, Giorgio Vittorio, Wolf, Jürgen, Planchard, David, Besse, Benjamin, Herbst, Roy S., Wakelee, Heather A., Pennell, Nathan A., Shaw, Alice T., Jänne, Pasi A., Carbone, David P., Hellmann, Matthew D., Rudin, Charles M., Albacker, Lee, Mann, Helen, Zhu, Zhou, Lai, Zhongwu, Stewart, Ross, Peters, Solange, Johnson, Melissa L., Wong, Kwok K., Huang, Alan, Winslow, Monte M., Rosen, Michael J., Winters, Ian P., Papadimitrakopoulou, Vassiliki A., Cascone, Tina, Jewsbury, Philip, Heymach, John V.
Publikováno v:
Nature; November 2024, Vol. 635 Issue: 8038 p462-471, 10p