Zobrazeno 1 - 10
of 350
pro vyhledávání: '"Done, Susan"'
Autor:
Dy, Amanda, Nguyen, Ngoc-Nhu Jennifer, Mirjahanmardi, Seyed Hossein, Dawe, Melanie, Fyles, Anthony, Shi, Wei, Liu, Fei-Fei, Androutsos, Dimitrios, Done, Susan, Khademi, April
Deep learning systems have been proposed to improve the objectivity and efficiency of Ki- 67 PI scoring. The challenge is that while very accurate, deep learning techniques suffer from reduced performance when applied to out-of-domain data. This is a
Externí odkaz:
http://arxiv.org/abs/2307.03872
Autor:
Mirjahanmardi, Seyed Hossein, Dawe, Melanie, Fyles, Anthony, Shi, Wei, Liu, Fei-Fei, Done, Susan, Khademi, April
Nuclei detection is a key task in Ki67 proliferation index estimation in breast cancer images. Deep learning algorithms have shown strong potential in nuclei detection tasks. However, they face challenges when applied to pathology images with dense m
Externí odkaz:
http://arxiv.org/abs/2111.05482
Autor:
Dawe, Melanie, Shi, Wei, Liu, Tian Y., Lajkosz, Katherine, Shibahara, Yukiko, Gopal, Nakita E.K., Geread, Rokshana, Mirjahanmardi, Seyed, Wei, Carrie X., Butt, Sehrish, Abdalla, Moustafa, Manolescu, Sabrina, Liang, Sheng-Ben, Chadwick, Dianne, Roehrl, Michael H.A., McKee, Trevor D., Adeoye, Adewunmi, McCready, David, Khademi, April, Liu, Fei-Fei, Fyles, Anthony, Done, Susan J.
Publikováno v:
In Laboratory Investigation May 2024 104(5)
The difficulty of detecting mitosis and its similarity to non-mitosis objects has remained a challenge in computational pathology. The lack of publicly available data has added more complexity. Deep learning algorithms have shown potentials in mitosi
Externí odkaz:
http://arxiv.org/abs/2109.01526
Mitotic counts are one of the key indicators of breast cancer prognosis. However, accurate mitotic cell counting is still a difficult problem and is labourious. Automated methods have been proposed for this task, but are usually dependent on the trai
Externí odkaz:
http://arxiv.org/abs/2109.01085
Autor:
Jaiswal, Arushi, Murakami, Kiichi, Elia, Andrew, Shibahara, Yukiko, Done, Susan J., Wood, Stephen A., Donato, Nicholas J., Ohashi, Pamela S., Reedijk, Michael
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2021 Sep . 118(38), 1-10.
Externí odkaz:
https://www.jstor.org/stable/27075667
Akademický článek
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Autor:
Razavi, Salar, Khameneh, Fariba D., Nouri, Hana, Androutsos, Dimitrios, Done, Susan J., Khademi, April
Publikováno v:
In Journal of Pathology Informatics 2022 13
Autor:
Gibson, Christopher, Wang, Shirley C., Phoon, Arcturus, Thalanki Anantha, Nayana, Ottolino-Perry, Kathryn, Petropoulos, Stephen, Qureshi, Zuha, Subramanian, Vasanth, Shahid, Anam, O'Brien, Cristiana, Carcone, Steven, Chung, Suzanne, Tsui, Teresa, Son, Viktor, Sukhram, Mayleen, Meng, Fannong, Done, Susan J., Easson, Alexandra M., Cil, Tulin, Reedijk, Michael
Publikováno v:
BMC Biomedical Engineering; 6/1/2024, Vol. 6 Issue 1, p1-20, 20p
Akademický článek
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