Convnextv2 Swin Kvasir Gi Endoscopy

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.

--- license: apache-2.0 tags: - medical-imaging - gi-endoscopy - kvasir - convnext - swin-transformer - cross-attention - pytorch - image-classification datasets: - kvasir-dataset-v2 metrics: - accuracy - f1 - roc_auc --- # 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) | Metric | Value | |---|---| | Accuracy | 0.9514 ± 0.0057 | | Precision (macro) | 0.9520 ± 0.0054 | | Recall (macro) | 0.9514 ± 0.0057 | | F1 (macro) | 0.9513 ± 0.0057 | | ROC-AUC (macro) | 0.9959 ± 0.0014 | ## Usage ```python 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.