Differential Attention-Augmented BiomedCLIP with Asymmetric Focal Optimization for Imbalanced Multi-Label Video Capsule Endoscopy Classification

This work presents a multi-label classification framework for video capsule endoscopy (VCE) that addresses the extreme class imbalance inherent in the Galar dataset through a combination of architectural and optimization-level strategies. Our approach modifies BiomedCLIP, a biomedical vision-language foundation model, by replacing its standard multi-head self-attention with a differential attention mechanism that computes the difference between two softmax attention maps to suppress attention noise. To counteract the skewed label distribution, where pathological findings constitute less than 0.1% of all annotated frames, a sqrt-frequency weighted sampler, asymmetric focal loss, mixup regularization, and per-class threshold optimization are employed. Temporal coherence is enforced through median-filter smoothing and gap merging prior to event-level JSON generation. On the held-out RARE-VISION test set comprising three NaviCam examinations (161,025 frames), the pipeline achieves an overall temporal mAP@0.5 of 0.2456 and mAP@0.95 of 0.2353, with total inference completed in approximately 8.6 minutes on a single GPU.