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pro vyhledávání: '"Pinchaud, Nicolas"'
Autor:
Pinchaud, Nicolas
We present a novel approach to train pixel resolution segmentation models on whole slide images in a weakly supervised setup. The model is trained to classify patches extracted from slides. This leads the training to be made under noisy labeled data.
Externí odkaz:
http://arxiv.org/abs/1905.12931
Autor:
Pinchaud, Nicolas
We investigate the effects of the unsupervised pre-training method under the perspective of information theory. If the input distribution displays multiple views of the supervision, then unsupervised pre-training allows to learn hierarchical represen
Externí odkaz:
http://arxiv.org/abs/1905.12889
Autor:
Pinchaud, Nicolas
We propose a novel objective function for learning robust deep representations of data based on information theory. Data is projected into a feature-vector space such that the mutual information of all subsets of features relative to the supervising
Externí odkaz:
http://arxiv.org/abs/1905.12874
Autor:
Burlutskiy, Nikolay, Pinchaud, Nicolas, Gu, Feng, Hägg, Daniel, Andersson, Mats, Björk, Lars, Eurén, Kristian, Svensson, Cristina, Wilén, Lena Kajland, Hedlund, Martin
Gleason grading specified in ISUP 2014 is the clinical standard in staging prostate cancer and the most important part of the treatment decision. However, the grading is subjective and suffers from high intra and inter-user variability. To improve th
Externí odkaz:
http://arxiv.org/abs/1904.06969
Akademický článek
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Autor:
Stadler, Caroline Bivik, Lindvall, Martin, Lundström, Claes, Bodén, Anna, Lindman, Karin, Rose, Jeronimo, Treanor, Darren, Blomma, Johan, Stacke, Karin, Pinchaud, Nicolas, Hedlund, Martin, Landgren, Filip, Woisetschläger, Mischa, Forsberg, Daniel
Publikováno v:
Journal of Digital Imaging; Feb2021, Vol. 34 Issue 1, p105-115, 11p, 5 Color Photographs, 1 Black and White Photograph, 2 Charts
