Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification
Accurate breast cancer classification from mammography requires effective integration of complementary information from craniocaudal (CC) and mediolateral oblique (MLO) views, which provide a more complete characterization of breast abnormalities. However, existing multi-view learning approaches typically rely on feature-level aggregation or single-stage cross-attention, which can entangle view-specific and shared representations and restrict interaction to limited network depths. To address these limitations, we propose a token-centric dual-view learning framework that unifies prompt-based ad
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 59%VioletVision-3B →
“Fuzzy title match (0.73): “Token-Based Dual-view Fusion and Adaptation of Large Vision ” ≈ “VioletVision-3B””
- LinkedLinked via arxiv author · 85%Aysan Ghayouri Pirsoltan →
“Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification”
- LinkedLinked via arxiv author · 85%Shima Babakordi →
“Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification”
- LinkedLinked via arxiv author · 85%Mohammad Reza Mohammadi →
“Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification”
- FuzzySimilar title/name (fuzzy) · 84%pytorch/vision →
“Fuzzy title match (0.92): “Token-Based Dual-view Fusion and Adaptation of Large Vision ” ≈ “pytorch/vision””
- PossiblePossibly related (embedding) · 54%Breast cancer detection and classification via a robust deep learning approach - Nature →