convnextv2-swin-kvasir-gi-endoscopy

ConvNeXt V2 + Swin Transformer + Cross-Attention Fusion — Kvasir Dataset v2 (8-class GI Endoscopy)

8-class classifier (dyed-lifted-polyps, dyed-resection-margins, esophagitis, normal-cecum, normal-pylorus, normal-z-line, polyps, ulcerative-colitis) for GI endoscopy frames, trained on Kvasir-Dataset-v2 with stratified group 10-fold cross-validation.

Architecture

• ConvNeXt V2 (tiny) backbone — local/texture features
• Swin Transformer (tiny) backbone — global/contextual features
• Bidirectional cross-attention fusion (2 layers) between both token streams
• MLP classification head

10-Fold Cross-Validation Results (mean ± std)

Usage

from huggingface_hub import hf_hub_download
# see inference.py in this repo for load_model() / predict()

Explainability

Grad-CAM visualizations for both the ConvNeXt and Swin streams are included in the original training notebook to support clinical interpretability.