# Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction

arXiv: https://arxiv.org/abs/2506.19363 · HTML: https://mlsanity.com/paper/2506.19363/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 24 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration](https://mlsanity.com/paper/2307.03421/) (2023)
- [Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images](https://mlsanity.com/paper/2303.15699/) (2023)
- [Longitudinal Mammogram Risk Prediction](https://mlsanity.com/paper/2404.19083/) (2024)
- [Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms](https://mlsanity.com/paper/2409.06887/) (2024)

## Built on by (most cited first, 6 of 6)

- [The Impact of Longitudinal Mammogram Alignment on Breast Cancer Risk Assessment](https://mlsanity.com/paper/2511.08328/) (2025)
- [Longitudinal Multi-View Breast Cancer Risk Prediction](https://mlsanity.com/paper/2607.11343/) (2026)
- [Mammo-CLIP Dissect: A Framework for Analysing Mammography Concepts in Vision-Language Models](https://mlsanity.com/paper/2509.21102/) (2025)
- [MGRegBench: A Novel Benchmark Dataset with Anatomical Landmarks for Mammography Image Registration](https://mlsanity.com/paper/2512.17605/) (2025)
- [Longitudinal Risk Prediction in Mammography with Privileged History Distillation](https://mlsanity.com/paper/2603.15814/) (2026)
- [Explicitly Aligned Multi-View Breast Cancer Risk Prediction from Variable-Length Screening Histories](https://mlsanity.com/paper/2610.16329/) (2026)

## Abstract (arXiv)

Regular mammography screening is essential for early breast cancer detection. Deep learning-based risk prediction methods have sparked interest to adjust screening intervals for high-risk groups. While early methods focused only on current mammograms, recent approaches leverage the temporal aspect of screenings to track breast tissue changes over time, requiring spatial alignment across different time points. Two main strategies for this have emerged: explicit feature alignment through deformable registration and implicit learned alignment using techniques like transformers, with the former providing more control. However, the optimal approach for explicit alignment in mammography remains underexplored. In this study, we provide insights into where explicit alignment should occur (input space vs. representation space) and if alignment and risk prediction should be jointly optimized. We demonstrate that jointly learning explicit alignment in representation space while optimizing risk estimation performance, as done in the current state-of-the-art approach, results in a trade-off between alignment quality and predictive performance and show that image-level alignment is superior to representation-level alignment, leading to better deformation field quality and enhanced risk prediction accuracy. The code is available at https://github.com/sot176/Longitudinal_Mammogram_Alignment.git.