Explicitly Aligned Multi-View Breast Cancer Risk Prediction from Variable-Length Screening Histories

Key points

• Accurate breast cancer risk prediction from screening mammography is essential for improved risk stratification and personalized screening.1
• It can benefit from both temporal breast tissue changes across longitudinal examinations and complementary anatomical information from craniocaudal (CC) and mediolateral oblique (MLO) views.1
• Leveraging longitudinal information requires identifying corresponding tissue regions across examinations, yet most approaches rely on implicit feature alignment without explicitly enforcing spatial consistency.1
• We further introduce (github.com/sot176/BCRBench), an open-source benchmark that unifies state-of-the-art single- and multi-timepoint breast cancer risk prediction models under standardized preprocessing, training, inference, and evaluation protocols, enabling fair and reproducible comparison of existing methods.1

Sources (1)

• [1]Explicitly Aligned Multi-View Breast Cancer Risk Prediction from Variable-Length Screening Histories
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 07:47 AM

  To fill these gaps, we propose (github.com/sot176/FlexLMV-Net), a breast-wise longitudinal multi-view framework that jointly models CC and MLO views across screening examinations while accommodating a variable number of prior examinations.

  Accurate breast cancer risk prediction from screening mammography is essential for improved risk stratification and personalized screening.

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