Zobrazeno 1 - 10
of 3 180
pro vyhledávání: '"A. Loening"'
Autor:
Swidsinski, Alexander, Amann, Rudolf, Guschin, Alexander, Swidsinski, Sonja, Loening-Baucke, Vera, Mendling, Werner, Sobel, Jack D., Lamont, Ronald F., Vaneechoutte, Mario, Baptista, Pedro Vieira, Bradshaw, Catriona S., Kogan, Igor Yu, Savicheva, Аlevtina M., Mitrokhin, Oleg V., Swidsinski, Nadezhda W., Sukhikh, Gennadiy T., Priputnevich, Tatjana V., Apolikhina, Inna A., Dörffel, Yvonne
Publikováno v:
In Microbes and Infection November-December 2024 26(8)
Autor:
Duan, Heying, Moradi, Farshad, Davidzon, Guido A, Liang, Tie, Song, Hong, Loening, Andreas M, Vasanawala, Shreyas, Srinivas, Sandy, Brooks, James D, Hancock, Steven, Iagaru, Andrei *
Publikováno v:
In The Lancet Oncology April 2024 25(4):501-508
Machine learning (ML) and AI toolboxes such as scikit-learn or Weka are workhorses of contemporary data scientific practice -- their central role being enabled by usable yet powerful designs that allow to easily specify, train and validate complex mo
Externí odkaz:
http://arxiv.org/abs/2101.04938
Autor:
Löning, Markus, Király, Franz
We present a new open-source framework for forecasting in Python. Our framework forms part of sktime, a more general machine learning toolbox for time series with scikit-learn compatible interfaces for different learning tasks. Our new framework prov
Externí odkaz:
http://arxiv.org/abs/2005.08067
Akademický článek
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Autor:
Eminaga, Okyaz, Tolkach, Yuri, Kunder, Christian, Abbas, Mahmood, Han, Ryan, Nolley, Rosalie, Semjonow, Axel, Boegemann, Martin, Huss, Sebastian, Loening, Andreas, West, Robert, Sonn, Geoffrey, Fan, Richard, Bettendorf, Olaf, Brook, James, Rubin, Daniel
The current study detects different morphologies related to prostate pathology using deep learning models; these models were evaluated on 2,121 hematoxylin and eosin (H&E) stain histology images captured using bright field microscopy, which spanned a
Externí odkaz:
http://arxiv.org/abs/1910.04918
Autor:
Löning, Markus, Bagnall, Anthony, Ganesh, Sajaysurya, Kazakov, Viktor, Lines, Jason, Király, Franz J.
We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series. Time series data gives rise to various distinct but closely related learning tasks, such as forecasting and time series
Externí odkaz:
http://arxiv.org/abs/1909.07872
sktime is an open source, Python based, sklearn compatible toolkit for time series analysis developed by researchers at the University of East Anglia (UEA), University College London and the Alan Turing Institute. A key initial goal for sktime was to
Externí odkaz:
http://arxiv.org/abs/1909.05738
Autor:
Eminaga, Okyaz, Abbas, Mahmoud, Kunder, Christian, Loening, Andreas M., Shen, Jeanne, Brooks, James D., Langlotz, Curtis P., Rubin, Daniel L.
Different convolutional neural network (CNN) models have been tested for their application in histological image analyses. However, these models are prone to overfitting due to their large parameter capacity, requiring more data or valuable computati
Externí odkaz:
http://arxiv.org/abs/1908.09067
Autor:
Peyman Shokrollahi, Juan M. Zambrano Chaves, Jonathan P. H. Lam, Avishkar Sharma, Debashish Pal, Naeim Bahrami, Akshay S. Chaudhari, Andreas M. Loening
Publikováno v:
IEEE Access, Vol 11, Pp 99222-99236 (2023)
Radiologists use an imaging order from the ordering physician, which includes a radiology title, to select the most suitable imaging protocol. Inappropriate radiology titles can disrupt protocol selection and result in mistaken or delayed diagnosis.
Externí odkaz:
https://doaj.org/article/a80a27e1f9314fa3b39f052a80e10956