all AI news
Scale dependant layer for self-supervised nuclei encoding. (arXiv:2207.10950v1 [cs.CV])
July 25, 2022, 1:12 a.m. | Peter Naylor, Yao-Hung Hubert Tsai, Marick Laé, Makoto Yamada
cs.CV updates on arXiv.org arxiv.org
Recent developments in self-supervised learning give us the possibility to
further reduce human intervention in multi-step pipelines where the focus
evolves around particular objects of interest. In the present paper, the focus
lays in the nuclei in histopathology images. In particular we aim at extracting
cellular information in an unsupervised manner for a downstream task. As nuclei
present themselves in a variety of sizes, we propose a new Scale-dependant
convolutional layer to bypass scaling issues when resizing nuclei. On three …
More from arxiv.org / cs.CV updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Data Architect
@ Western Digital | San Jose, CA, United States
Senior Data Scientist GenAI (m/w/d)
@ Deutsche Telekom | Bonn, Deutschland
Senior Data Engineer, Telco (Remote)
@ Lightci | Toronto, Ontario
Consultant Data Architect/Engineer H/F - Innovative Tech
@ Devoteam | Lyon, France
(Senior) ML Engineer / Software Engineer Machine Learning & AI (m/f/x) onsite or remote (in Germany or Austria)
@ Scalable GmbH | Wien, Germany