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Integrating AI for Human-Centric Breast Cancer Diagnostics: A Multi-Scale and Multi-View Swin Transformer Framework
ArXiv.org
17 Mar 2025
Abstract
Despite advancements in Computer-Aided Diagnosis (CAD) systems, breast cancer
remains one of the leading causes of cancer-related deaths among women
worldwide. Recent breakthroughs in Artificial Intelligence (AI) have shown
significant promise in development of advanced Deep Learning (DL) architectures
for breast cancer diagnosis through mammography. In this context, the paper
focuses on the integration of AI within a Human-Centric workflow to enhance
breast cancer diagnostics. Key challenges are, however, largely overlooked such
as reliance on detailed tumor annotations and susceptibility to missing views,
particularly during test time. To address these issues, we propose a hybrid,
multi-scale and multi-view Swin Transformer-based framework (MSMV-Swin) that
enhances diagnostic robustness and accuracy. The proposed MSMV-Swin framework
is designed to work as a decision-support tool, helping radiologists analyze
multi-view mammograms more effectively. More specifically, the MSMV-Swin
framework leverages the Segment Anything Model (SAM) to isolate the breast
lobe, reducing background noise and enabling comprehensive feature extraction.
The multi-scale nature of the proposed MSMV-Swin framework accounts for
tumor-specific regions as well as the spatial characteristics of tissues
surrounding the tumor, capturing both localized and contextual information. The
integration of contextual and localized data ensures that MSMV-Swin's outputs
align with the way radiologists interpret mammograms, fostering better human-AI
interaction and trust. A hybrid fusion structure is then designed to ensure
robustness against missing views, a common occurrence in clinical practice when
only a single mammogram view is available.
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Details
- Title
- Integrating AI for Human-Centric Breast Cancer Diagnostics: A Multi-Scale and Multi-View Swin Transformer Framework
- Creators
- Farnoush BayatmakouReza TaleeiMilad Amir ToutounchianArash Mohammadi
- Publication Details
- ArXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Information Science
- Other Identifier
- 991022041190904721