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Robust multi-vendor breast region segmentation using deep learning

Conclusion: Transpara can provide robust breast region segmentations in a multimodal multi-vendor setting.

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Artificial intelligence together with mechanical imaging in mammography

  Conclusion: Mechanical imaging (MI) estimates the relative stiffness of suspicious breast abnormalities by measuring the distribution of pressure on

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Can AI serve as an independent second reader of mammograms? a simulation study

Conclusion: Using Transpara as a second reader in a double reading setting, workload could be reduced by 44%, without an

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Personalised breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence

Conclusion: Using DBT only for cases marked as highly suspicious by Transpara in mammography could be an alternative to a

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The effect of breast density on the performance of deep learning-based breast cancer detection methods for mammography

Conclusion: Transpara risk categorization is not affected by density. Transpara has the potential to improve screening sensitivity even for women

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Going from double to single reading for screening exams labeled as likely normal by AI: what is the impact?

Conclusion: Transpara can improve breast cancer screening efficiency by pre-selecting likely normal exams where double-reading can be safely replaced by

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