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New LMV-Net model improves breast cancer risk prediction using longitudinal mammography data

By PulseAugur Editorial · [2 sources] ·

Researchers have developed LMV-Net, a novel deep learning model for predicting breast cancer risk using longitudinal mammography data. This model jointly analyzes complementary CC and MLO views within an explicitly aligned longitudinal framework, addressing limitations of previous methods that either used single views or lacked explicit temporal alignment. Evaluations on public datasets showed LMV-Net consistently outperformed existing approaches, highlighting its potential for enhanced risk stratification and personalized screening. AI

IMPACT This model's improved risk stratification could lead to more personalized screening and earlier detection of high-risk patients.

RANK_REASON The cluster contains a research paper detailing a new model and its evaluation.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New LMV-Net model improves breast cancer risk prediction using longitudinal mammography data

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The cluster contains a research paper detailing a new model and its evaluation.
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2 independent sources
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paper, model release
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57 days old
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Solveig Thrun, Zijun Sun, Suaiba A. Salahuddin, Kristoffer Wickstr{\o}m, Elisabeth Wetzer, Stine Hansen, Robert Jenssen, Michael Kampffmeyer ·

    Longitudinal Multi-View Breast Cancer Risk Prediction

    arXiv:2607.11343v1 Announce Type: cross Abstract: Accurate breast cancer risk prediction from screening mammography is critical for enabling personalized screening intervals and early detection. Recent deep learning methods have shown the value of longitudinal data and explicit t…

  2. arXiv cs.AI TIER_1 English(EN) · Michael Kampffmeyer ·

    Longitudinal Multi-View Breast Cancer Risk Prediction

    Accurate breast cancer risk prediction from screening mammography is critical for enabling personalized screening intervals and early detection. Recent deep learning methods have shown the value of longitudinal data and explicit temporal alignment. However, existing approaches ei…

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