A combination of task interpolation, relational embedding, and bi-level routing attention reaches 90.1% accuracy on the Kvasir GI image classification benchmark.
Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video),
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
REJECT 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
Lightweight Relational Embedding in Task-Interpolated Few-Shot Networks for Enhanced Gastrointestinal Disease Classification
A combination of task interpolation, relational embedding, and bi-level routing attention reaches 90.1% accuracy on the Kvasir GI image classification benchmark.