Search Results for author:
Found 3 papers, 1 papers with code
Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
It takes advantage of MatchExplainer to fix the most informative portion of the graph and merely operates graph augmentations on the rest less informative part.
Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images
The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis.
Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images
An outbreak of a novel coronavirus disease (i. e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
