Zobrazeno 1 - 10
of 196
pro vyhledávání: '"Peter Maass"'
Publikováno v:
Environmental Data Science, Vol 2 (2023)
In climate modeling, the stratospheric ozone layer is typically only considered in a highly simplified form due to computational constraints. For climate projections, it would be of advantage to include the mutual interactions between stratospheric o
Externí odkaz:
https://doaj.org/article/1f7ee32237ab41e2af34a5df0e513c7d
Publikováno v:
Scientific Data, Vol 8, Iss 1, Pp 1-12 (2021)
Measurement(s) Low Dose Computed Tomography of the Chest • feature extraction objective Technology Type(s) digital curation • image processing technique Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the
Externí odkaz:
https://doaj.org/article/6a2f386a28994aa6a8db73f70a355873
Publikováno v:
Journal of Imaging, Vol 8, Iss 7, p 202 (2022)
In recent years, numerous deep-learning approaches have been developed for the analysis of histopathology Whole Slide Images (WSI). A recurrent issue is the lack of generalization ability of a model that has been trained with images of one laboratory
Externí odkaz:
https://doaj.org/article/54b8e27fa5e9433cac9bc5df14b3531f
Publikováno v:
Journal of Imaging, Vol 7, Iss 11, p 243 (2021)
Over recent years, deep learning methods have become an increasingly popular choice for solving tasks from the field of inverse problems. Many of these new data-driven methods have produced impressive results, although most only give point estimates
Externí odkaz:
https://doaj.org/article/0a745086f3d24cb58d64562b47d9acde
Autor:
Jean Le’Clerc Arrastia, Nick Heilenkötter, Daniel Otero Baguer, Lena Hauberg-Lotte, Tobias Boskamp, Sonja Hetzer, Nicole Duschner, Jörg Schaller, Peter Maass
Publikováno v:
Journal of Imaging, Vol 7, Iss 4, p 71 (2021)
Accurate and fast assessment of resection margins is an essential part of a dermatopathologist’s clinical routine. In this work, we successfully develop a deep learning method to assist the dermatopathologists by marking critical regions that have
Externí odkaz:
https://doaj.org/article/4ce6abaf685c4ae9b692fe1cc3c512f4
Autor:
Johannes Leuschner, Maximilian Schmidt, Poulami Somanya Ganguly, Vladyslav Andriiashen, Sophia Bethany Coban, Alexander Denker, Dominik Bauer, Amir Hadjifaradji, Kees Joost Batenburg, Peter Maass, Maureen van Eijnatten
Publikováno v:
Journal of Imaging, Vol 7, Iss 3, p 44 (2021)
The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a data challenge tha
Externí odkaz:
https://doaj.org/article/a9c9ce94377745e8adb2466faaf9be04
Publikováno v:
Abstract and Applied Analysis, Vol 2008 (2008)
Tikhonov functionals are known to be well suited for obtaining regularized solutions of linear operator equations. We analyze two iterative methods for finding the minimizer of norm-based Tikhonov functionals in Banach spaces. One is the steepest des
Externí odkaz:
https://doaj.org/article/2609ed6c15844aba919413de4ff04c58
This open access book contains the research report of the Collaborative Research Center “Micro Cold Forming” (SFB 747) of the University of Bremen, Germany. The topical research focus lies on new methods and processes for a mastered mass producti
Autor:
Janek Gödeke, Hyunkee Hong, Andreas Richter, Peter Maaß, Kezia Lange, Hanlim Lee, Junsung Park
Recent works on using Machine Learning methods for deriving estimates of the NO2 concentration at the Earth's surface from satellite observations exploit measurements taken from low Earth orbits, e.g. from the TROPOMI instrument on the Copernicus Sen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::284e08cea9032c2395f05dc22d098f38
https://doi.org/10.5194/egusphere-egu23-9309
https://doi.org/10.5194/egusphere-egu23-9309
Publikováno v:
Oberwolfach Reports. 18:745-789
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5eb032a10a458d22c164923288545f66
https://doi.org/10.4171/owr/2021/13
https://doi.org/10.4171/owr/2021/13