# Longitudinal Multi-View Breast Cancer Risk Prediction

arXiv: https://arxiv.org/abs/2607.11343 · HTML: https://mlsanity.com/paper/2607.11343/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Deep Networks with Stochastic Depth](https://mlsanity.com/paper/1603.09382/) (2016)
- [Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images](https://mlsanity.com/paper/2303.15699/) (2023)
- [Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms](https://mlsanity.com/paper/2409.06887/) (2024)
- [Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction](https://mlsanity.com/paper/2506.19363/) (2025)
- [Breast Cancer Detection from Multi-View Screening Mammograms with Visual Prompt Tuning](https://mlsanity.com/paper/2504.19900/) (2025)
- [VMRA-MaR: An Asymmetry-Aware Temporal Framework for Longitudinal Breast Cancer Risk Prediction](https://mlsanity.com/paper/2506.17412/) (2025)
- [The Impact of Longitudinal Mammogram Alignment on Breast Cancer Risk Assessment](https://mlsanity.com/paper/2511.08328/) (2025)

## Built on by (most cited first, 1 of 1)

- [Explicitly Aligned Multi-View Breast Cancer Risk Prediction from Variable-Length Screening Histories](https://mlsanity.com/paper/2610.16329/) (2026)

## Abstract (arXiv)

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 either perform explicit alignment using a single mammographic view or model multiple views without explicit longitudinal alignment, limiting their ability to exploit the complementary spatial-temporal information used in clinical practice. To address this gap, we propose LMV-Net, a longitudinal multi-view breast cancer risk prediction model that jointly analyzes anatomically complementary CC and MLO views within an explicitly aligned longitudinal framework. We evaluate our approach on the public EMBED and CSAW-CC datasets, comparing it to state-of-the-art breast cancer risk prediction methods. Our model consistently outperforms existing approaches in overall risk prediction performance and across different breast density and cancer subgroups. Importantly, these improvements highlight the potential of longitudinal multi-view modeling to enhance risk stratification, paving the way for future work on personalized screening, earlier identification of high-risk patients, and more efficient screening resource allocation. The code is available at https://github.com/sot176/LMV-Net.