Zobrazeno 1 - 10
of 1 006
pro vyhledávání: '"Tolkach, A"'
Traditional initialisation methods, e.g. He and Xavier, have been effective in avoiding the problem of vanishing or exploding gradients in neural networks. However, they only use simple pointwise distributions, which model one-dimensional variables.
Externí odkaz:
http://arxiv.org/abs/2310.16695
Federated and Continual Learning have emerged as potential paradigms for the robust and privacy-aware use of Deep Learning in dynamic environments. However, Client Drift and Catastrophic Forgetting are fundamental obstacles to guaranteeing consistent
Externí odkaz:
http://arxiv.org/abs/2309.00688
Autor:
Eminaga, Okyaz, Abbas, Mahmoud, Kunder, Christian, Tolkach, Yuri, Han, Ryan, Brooks, James D., Nolley, Rosalie, Semjonow, Axel, Boegemann, Martin, West, Robert, Long, Jin, Fan, Richard, Bettendorf, Olaf
Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, vPatho, on
Externí odkaz:
http://arxiv.org/abs/2308.11992
Autor:
Sebastian Klein, Yuri Tolkach, Hans Christian Reinhardt, Reinhard Buettner, Alexander Quaas, Doris Helbig
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Pleomorphic dermal sarcomas are infrequent neoplastic skin tumors, manifesting in regions of the skin exposed to ultraviolet radiation. Diagnosing the entity can be challenging and therapeutic options are limited. We analyzed 20 samples of n
Externí odkaz:
https://doaj.org/article/ca774a0e925240db82fb5be795d691b9
Although deep federated learning has received much attention in recent years, progress has been made mainly in the context of natural images and barely for computational pathology. However, deep federated learning is an opportunity to create datasets
Externí odkaz:
http://arxiv.org/abs/2209.14849
Autor:
Okyaz Eminaga, Mahmoud Abbas, Christian Kunder, Yuri Tolkach, Ryan Han, James D. Brooks, Rosalie Nolley, Axel Semjonow, Martin Boegemann, Robert West, Jin Long, Richard E. Fan, Olaf Bettendorf
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-30 (2024)
Abstract Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, v
Externí odkaz:
https://doaj.org/article/378b253abe26462a92e7ddbdb8d64e5c
Publikováno v:
Сибирский лесной журнал, Vol 11, Iss 1, Pp 78-89 (2024)
The comparison of snow density in small open spaces adjacent to forest stands and under the canopy of spruce stands located on the macroslopes of the Middle Urals of the eastem end westem expositions was carried out. It was found that the density of
Externí odkaz:
https://doaj.org/article/a1e6f0eb23fc4b45baf80560024afa9b
Publikováno v:
Сучасна педіатрія: Україна, Iss 7(135), Pp 122-135 (2023)
Мета - на підставі розбору клінічного випадку проаналізувати провідні маркери первинних імунодефіцитів у дітей із суглобовим синдромо
Externí odkaz:
https://doaj.org/article/8b82fb4993e946bea97d0f87c0927197
Autor:
Andreas H. Scheel, Hannah Lamberty, Yuri Tolkach, Florian Gebauer, Birgid Schoemig-Markiefka, Thomas Zander, Reinhard Buettner, Josef Rueschoff, Christiane Josephine Bruns, Wolfgang Schroeder, Alexander Quaas
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-10 (2023)
Abstract Guidelines regulate how many (tumour-bearing) tissue particles should be sampled during gastric cancer biopsy to obtain representative results in predictive biomarker testing. Little is known about how well these guidelines are applied, how
Externí odkaz:
https://doaj.org/article/3fa29354b4e7437cae6b5f805d6c5c1a
Autor:
Jin-On Jung, Juan I. Pisula, Xenia Beyerlein, Leandra Lukomski, Karl Knipper, Aram P. Abu Hejleh, Hans F. Fuchs, Yuri Tolkach, Seung-Hun Chon, Henrik Nienhüser, Markus W. Büchler, Christiane J. Bruns, Alexander Quaas, Katarzyna Bozek, Felix Popp, Thomas Schmidt
Publikováno v:
Cancers, Vol 16, Iss 13, p 2445 (2024)
Background: The aim of this study was to establish a deep learning prediction model for neoadjuvant FLOT chemotherapy response. The neural network utilized clinical data and visual information from whole-slide images (WSIs) of therapy-naïve gastroes
Externí odkaz:
https://doaj.org/article/0582bddeba064276bb702a46e059a239