# Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net

arXiv: https://arxiv.org/abs/1808.08885 · HTML: https://mlsanity.com/paper/1808.08885/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Deep Active Contours Using Locally Controlled Distance Vector Flow](https://mlsanity.com/paper/2105.08447/) (2021)
- [ReFace: Real-time Adversarial Attacks on Face Recognition Systems](https://mlsanity.com/paper/2206.04783/) (2022)
- [ConnectedUNets++: Mass Segmentation from Whole Mammographic Images](https://mlsanity.com/paper/2210.13668/) (2022)
- [BiLoG-Net: A Bi-Context Location-Guided Network for Breast Mass Segmentation and Malignancy Classification in Mammography](https://mlsanity.com/paper/2607.10188/) (2026)

## Abstract (arXiv)

We explore the use of deep learning for breast mass segmentation in mammograms. By integrating the merits of residual learning and probabilistic graphical modelling with standard U-Net, we propose a new deep network, Conditional Residual U-Net (CRU-Net), to improve the U-Net segmentation performance. Benefiting from the advantage of probabilistic graphical modelling in the pixel-level labelling, and the structure insights of a deep residual network in the feature extraction, the CRU-Net provides excellent mass segmentation performance. Evaluations based on INbreast and DDSM-BCRP datasets demonstrate that the CRU-Net achieves the best mass segmentation performance compared to the state-of-art methodologies. Moreover, neither tedious pre-processing nor post-processing techniques are not required in our algorithm.