HybridVision Transformers for Multi-scale Morphological Assessment in Precision Mammography

Mammographic breast cancer detection requires the simultaneous evaluation of multi-scale morphological features, from microcalcifications and margin irregularities to macroscopic architectural distortion and bilateral asymmetry. Conventional convolutional neural networks (CNNs) effectively isolate localized textures but struggle to model long-range contextual relationships. Conversely, Vision Transformers (ViT) excel at global structural reasoning but lack the inductive biases necessary for fine-scale feature extraction. Hybrid CNN-ViT architectures resolve this computational dichotomy by merging convolutional locality with transformer-based self-attention. This narrative review examines the biological, radiological, and computational foundations of hybrid CNN-ViT systems in precision mammography. We map the multi-scale morphological spectrum of breast cancer, trace the architectural evolution of deep learning models toward hybrid frameworks, and evaluate core engineering strategies including hierarchical tokenization, cross-scale attention, bilateral multi-view fusion, and computationally efficient high-resolution optimization. Reported performance advantages over single-stream architectures derive almost entirely from retrospective benchmark evaluation on small, curated datasets; cross-study comparison is confounded by heterogeneous task definitions, mixed imaging modalities, and absent prospective validation. Clinical deployment is additionally constrained by inference latency, performance disparities across demographic and density subgroups, and regulatory pathways that currently lack outcome-level evidence requirements for this architecture class. Rather than cataloguing peak reported results, this review maps each architectural claim to its biological rationale, imaging modality, and evidence class, identifying where the evidence currently stops.