arXiv:2606.28537v1 Announce Type: cross Abstract: Multiview mammography relies on paired craniocaudal (CC) and mediolateral oblique (MLO) views to provide complementary projections of a 3D breast volume, enabling precise anomaly localization. However, acquiring high-quality, balanced datasets remains challenging for deep learning applications. We propose a novel method to synthesize multiview mammograms by leveraging the inherent geometric relationship between CC and MLO views. To enforce an implicit 3D consistency prior during generation, we develop an alignment module that searches a 2D affi
The continuous advancements in AI and generative models are enabling increasingly sophisticated applications in medical imaging synthesis, pushing the boundaries of what is possible in diagnostic aid.
Improving the synthesis of medical images can address dataset limitations in deep learning for diagnostics, potentially accelerating research, training, and the development of more accurate AI diagnostic tools.
The ability to synthesize anatomically consistent multiview mammograms could significantly expand available data for AI model training, potentially leading to earlier and more accurate breast cancer detection.
- · AI medical imaging developers
- · Breast cancer diagnostic companies
- · Medical research institutions
- · Patients
- · Traditional medical imaging data acquisition methods
- · Companies reliant on scarce, proprietary medical datasets
Increased availability of high-quality synthetic medical imaging data for AI model development and validation.
Faster innovation cycles for AI-powered diagnostic tools, potentially reducing development costs and time-to-market.
Enhanced AI diagnostic accuracy could lead to improved patient outcomes and more personalized treatment pathways for breast cancer.
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