Active Learning with HRNet

Deep High-Resolution Representation Learning for Visual Recognition

The Helmholtz-Zentrum Hereon is operating several tomography end stations at the beamlines P05 and P07 of the synchrotron radiation facility PETRA III at DESY in Hamburg, Germany. Attenuation and phase contrast imaging techniques are provided as well as sample environments for in situ/operando/vivo experiments for applications in biology, medicine, materials science, etc. Very large and diverse data sets with varying spatiotemporal resolution, noise levels and artifacts are acquired which are challenging to process and analyze. Here we report on an active learning approach for the semantic segmentation of tomography data using a guided and interactive framework, and evaluate different acquisition functions for the selection of images to be annotated in the iterative process.
Documentation: https://github.com/HRNet/HRNet-Semantic-Segmentation
Source: https://github.com/HRNet/HRNet-Semantic-Segmentation
Jupyter: please create your own custom kernel for https://max-jhub.desy.de
Module: module load maxwell mdlma/HRnet

Description:
A HRnet based active learning approach for the semantic segmentation of tomography data using a guided and interactive framework, and evaluate different acquisition functions for the selection of images to be annotated in the iterative process.

Citation:
Wang J. et al., Deep High-Resolution Representation Learning for Visual Recognition, arXiv:1908.07919 [cs.CV]
Bashir Kazimi et al., An active learning approach for the interactive and guided segmentation of tomography data, Proceedings Volume 12242, Developments in X-Ray Tomography XIV