Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification
arXiv:2607.06309v1 Announce Type: cross Abstract: 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-vi
The paper leverages recent advancements in Large Vision Models and token-centric fusion techniques, which are rapidly evolving fields in AI and computer vision.
Improved breast cancer classification using advanced AI could lead to earlier and more accurate diagnoses, significantly impacting public health and healthcare efficiency.
This research proposes a new methodology for integrating multi-view mammography data, potentially overcoming current limitations in early breast cancer detection systems.
- · Healthcare sector
- · Patients at risk of breast cancer
- · AI/ML researchers in medical imaging
- · Diagnostic imaging companies
- · Traditional diagnostic methods
- · Companies with less sophisticated AI diagnostic tools
More accurate and earlier detection of breast cancer from mammograms.
Reduced rates of misdiagnosis, leading to improved patient outcomes and reduced healthcare costs.
Enhanced trust and adoption of AI in critical medical diagnostic applications, paving the way for broader deployment across other medical imaging domains.
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Read at arXiv cs.AI