A combination of task interpolation, relational embedding, and bi-level routing attention reaches 90.1% accuracy on the Kvasir GI image classification benchmark.
A novel multi-feature fusion method for classification of gastrointestinal diseases using endoscopy images,
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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.