Zobrazeno 1 - 10
of 113
pro vyhledávání: '"Teófilo, E."'
Autor:
de Araujo, Pedro H. Luz, de Almeida, Ana Paula G. S., Braz, Fabricio A., da Silva, Nilton C., Vidal, Flavio de Barros, de Campos, Teofilo E.
Publikováno v:
International Journal on Document Analysis and Recognition.2022
The Brazilian Supreme Court receives tens of thousands of cases each semester. Court employees spend thousands of hours to execute the initial analysis and classification of those cases -- which takes effort away from posterior, more complex stages o
Externí odkaz:
http://arxiv.org/abs/2207.00748
In this paper, we evaluate the effects of occlusions in the performance of a face recognition pipeline that uses a ResNet backbone. The classifier was trained on a subset of the CelebA-HQ dataset containing 5,478 images from 307 classes, to achieve t
Externí odkaz:
http://arxiv.org/abs/2109.09083
In the world where big data reigns and there is plenty of hardware prepared to gather a huge amount of non structured data, data acquisition is no longer a problem. Surveillance cameras are ubiquitous and they capture huge numbers of people walking a
Externí odkaz:
http://arxiv.org/abs/2106.15693
Autor:
Zosa, Teofilo E.
Deep learning has made a remarkable impact in the field of natural image processing over the past decade. Consequently, there is a great deal of interest in replicating this success across unsolved tasks in related domains, such as medical image anal
Externí odkaz:
http://arxiv.org/abs/2103.14969
Unsupervised Domain Adaptation (UDA) methods for person Re-Identification (Re-ID) rely on target domain samples to model the marginal distribution of the data. To deal with the lack of target domain labels, UDA methods leverage information from label
Externí odkaz:
http://arxiv.org/abs/2101.01215
Autor:
de Lucena Drumond, Patricia Medyna Lauritzen, Leite, Lindeberg Pessoa, de Campos, Teofilo E., Braz, Fabricio Ataides
Publikováno v:
In Engineering Applications of Artificial Intelligence June 2023 122
Autor:
Guth, Fred, deCampos, Teofilo E.
In this paper we approach the problem of skin lesion segmentation using a convolutional neural network based on the U-Net architecture. We present a set of training strategies that had a significant impact on the performance of this model. We evaluat
Externí odkaz:
http://arxiv.org/abs/1811.11314
Autor:
Veta, Mitko, van Diest, Paul J., Willems, Stefan M., Wang, Haibo, Madabhushi, Anant, Cruz-Roa, Angel, Gonzalez, Fabio, Larsen, Anders B. L., Vestergaard, Jacob S., Dahl, Anders B., Cireşan, Dan C., Schmidhuber, Jürgen, Giusti, Alessandro, Gambardella, Luca M., Tek, F. Boray, Walter, Thomas, Wang, Ching-Wei, Kondo, Satoshi, Matuszewski, Bogdan J., Precioso, Frederic, Snell, Violet, Kittler, Josef, de Campos, Teofilo E., Khan, Adnan M., Rajpoot, Nasir M., Arkoumani, Evdokia, Lacle, Miangela M., Viergever, Max A., Pluim, Josien P. W.
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting
Externí odkaz:
http://arxiv.org/abs/1411.5825
Akademický článek
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Autor:
Cahn, PE, Cassetti, I, Losso, M, Bloch, MT, Roth, N, McMahon, J, Moore, RJ, Smith, D, Clumeck, N, Vanderkerckhove, L, Vandercam, B, Moutschen, M, Baril, J, Conway, B, Smaill, F, Smith, GHR, Rachlis, A, Walmsley, SL, Perez, C, Wolff, M, Lasso, MF, Chahin, CE, Velez, JD, Sussmann, O, Reynes, J, Katlama, C, Yazdanpanah, Y, Ferret, S, Durant, J, Duvivier, C, Poizot-Martin, I, Ajana, F, Rockstroh, JK, Faetkanheuer, G, Esser, S, Jaeger, H, Degen, O, Bickel, M, Bogner, J, Arasteh, K, Hartl, H, Stoehr, A, Rojas, EM, Arathoon, E, Gonzalez, LD, Mejia, CR, Shahar, E, Turner, D, Levy, I, Sthoeger, Z, Elinav, H, Gori, A, Monforte, A D'Arminio, Di Perri, G, Lazzarin, A, Rizzardini, G, Antinori, A, Celesia, BM, Maggiolo, F, Chow, TS, Lee, CKC, Azwa, R Iskandar Shah Raja, Mustafa, M, Oyanguren, M, Castillo, RA, Hercilla, L, Echiverri, C, Maltez, F, da Cunha, JG Saraiva, Neves, I, Teofilo, E, Serrao, R, Nagimova, F, Khaertynova, I, Orlova-Morozova, E, Voronin, E, Sotnikov, V, Yakovlev, AA, Zakharova, NG, Tsybakova, OA, Botes, ME, Mohapi, L, Kaplan, R, Rassool, MS, Arribas, JR, Gatell, JM, Negredo, E, Ortega, E, Troya, J, Berenguer, J, Aguirrebengoa, K, Antela, A, Calmy, A, Cavassini, M, Rauch, A, Stoeckle, M, Sheng, WH, Lin, HH, Tsai, HC, Changpradub, D, Avihingsanon, A, Kiertiburanakul, S, Ratanasuwan, W, Nelson, MR, Clarke, A, Ustianowski, A, Winston, A, Johnson, MA, Asmuth, DM, Cade, J, Gallant, JE, Ruane, PJ, Kumar, PN, Luque, AE, Panther, L, Tashima, KT, Ward, D, Berger, DS, Dietz, CA, Fichtenbaum, C, Gupta, S, Mullane, KM, Novak, RM, Sweet, DE, Crofoot, GE, Hagins, DP, Lewis, ST, McDonald, CK, DeJesus, E, Sloan, L, Prelutsky, DJ, Rondon, JC, Henn, S, Scarsella, AJ, Morales, JO, Ramirez, Santiago, L, Zorrilla, CD, Saag, MS, Hsiao, CB, Cahn, Pedro, Kaplan, Richard, Sax, Paul E, Squires, Kathleen, Molina, Jean-Michel, Avihingsanon, Anchalee, Ratanasuwan, Winai, Rojas, Evelyn, Rassool, Mohammed, Bloch, Mark, Vandekerckhove, Linos, Ruane, Peter, Yazdanpanah, Yazdan, Katlama, Christine, Xu, Xia, Rodgers, Anthony, East, Lilly, Wenning, Larissa, Rawlins, Sandy, Homony, Brenda, Sklar, Peter, Nguyen, Bach-Yen, Leavitt, Randi, Teppler, Hedy
Publikováno v:
In The Lancet HIV November 2017 4(11):e486-e494