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arXiv Computer Vision By Sadhana Devarajan, Praveen Kumar Chandaliya, Dhruvin Jashvant Kumar Shah, Kishor Upla, Kiran Raja

MultiAttenGastro: Multi-Dimensional Attention Augmentation for Gastrointestinal Endoscopy Classification

Read the original on arXiv Computer Vision →

MultiAttenGastro is a plug‑and‑play attention framework that adds parallel 1‑D channel, 2‑D spatial, and 3‑D contextual heads to existing CNN and transformer backbones for gastrointestinal endoscopy classification. Across eight backbones and five public GI datasets, the framework improves performance on large‑gap datasets such as Kvasir‑Capsule but shows no benefit on small‑gap benchmarks like Kvasir‑v2, with mixed results elsewhere. Analysis using Centered Kernel Alignment indicates that the gains are linked to representational redundancy: low inter‑head redundancy under large domain gaps yields consistent improvements, while high redundancy under small gaps leads to losses.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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