# MammoFlow: Multiview Mammogram Synthesis with Anatomically Consistent Flow Matching

arXiv: https://arxiv.org/abs/2606.28537 · HTML: https://mlsanity.com/paper/2606.28537/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 26 Jun 2026.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 14 of 14)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography](https://mlsanity.com/paper/2203.11205/) (2022)
- [Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography](https://mlsanity.com/paper/2405.12255/) (2024)
- [Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets](https://mlsanity.com/paper/2412.01496/) (2024)
- [Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network](https://mlsanity.com/paper/2110.01606/) (2021)
- [High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection](https://mlsanity.com/paper/2209.09809/) (2022)
- [MAM-E: Mammographic synthetic image generation with diffusion models](https://mlsanity.com/paper/2311.09822/) (2023)
- [CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer](https://mlsanity.com/paper/2112.01330/) (2021)
- [Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classification](https://mlsanity.com/paper/2306.10676/) (2023)
- [Bidirectional Mammogram View Translation with Column-Aware and Implicit 3D Conditional Diffusion](https://mlsanity.com/paper/2510.04947/) (2025)
- [MammoRGB: Dual-View Mammogram Synthesis Using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2511.22759/) (2025)

## Abstract (arXiv)

Multiview mammography relies on paired craniocaudal (CC) and mediolateral oblique (MLO) views to provide complementary projections of a 3D breast volume, enabling precise anomaly localization. However, acquiring high-quality, balanced datasets remains challenging for deep learning applications. We propose a novel method to synthesize multiview mammograms by leveraging the inherent geometric relationship between CC and MLO views. To enforce an implicit 3D consistency prior during generation, we develop an alignment module that searches a 2D affine transformation subspace to establish optimal anatomical correspondence. Leveraging this alignment, we introduce a pixel-space self-consistency loss based on the Earth Mover's Distance (EMD) between the 1D anteroposterior (AP) axis tissue distributions of the generated images. Integrated into a pretrained flow matching model, MammoFlow forces synthesized pairs to share physically plausible tissue distributions from the chest wall to the nipple. To our knowledge, this is the first work to guide multiview mammogram generation using implicit geometric tissue correspondence. Our method demonstrates superior image quality, passes expert radiologist evaluation, and generates physically consistent pairs that improve downstream classification AUC by 5%. Code is available at https://github.com/XYPB/MammoFlow