H-SemiS decomposes multi-class KOA severity grading into binary sub-tasks in a semi-supervised setup with self-supervision and quantum-inspired mixing, outperforming baselines on two multi-class and two binary datasets.
author Patravali, P
2 Pith papers cite this work, alongside 27 external citations. Polarity classification is still indexing.
2
Pith papers citing it
27
external citations · OpenAlex
citation-role summary
method 1
citation-polarity summary
fields
cs.CV 2years
2026 2roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
H-SemiS: Hierarchical Fusion of Semi and Self-Supervised Learning for Knee Osteoarthritis Severity Grading
H-SemiS decomposes multi-class KOA severity grading into binary sub-tasks in a semi-supervised setup with self-supervision and quantum-inspired mixing, outperforming baselines on two multi-class and two binary datasets.